U.S. patent number 9,507,047 [Application Number 13/465,105] was granted by the patent office on 2016-11-29 for method and system for integrating logging tool data and digital rock physics to estimate rock formation properties.
This patent grant is currently assigned to Ingrain, Inc.. The grantee listed for this patent is Jack Dvorkin, David Malone, Carl Sisk, Henrique Tono. Invention is credited to Jack Dvorkin, David Malone, Carl Sisk, Henrique Tono.
United States Patent |
9,507,047 |
Dvorkin , et al. |
November 29, 2016 |
**Please see images for:
( Certificate of Correction ) ** |
Method and system for integrating logging tool data and digital
rock physics to estimate rock formation properties
Abstract
The present invention relates to a method and system for
integrating logging tool data and digital rock physics to estimate
rock formation properties. A rock sample from a logging tool such
as a sidewall plug or large enough cutting can be extracted by the
logging tool at approximately the same well bore location that the
logging tool measures fluid properties. The rock samples thus
obtained is scanned using a CT scanner, scanning electron
microscope or other suitable scanning device. The resulting scanned
rock image can be segmented and rock properties comprising
porosity, absolute permeability, relative permeability, capillary
pressure and other relevant rock properties are calculated. The
resulting digital calculations are integrated with logging tool
data and rock property estimates to improve the accuracy and
timeliness of the logging tool data.
Inventors: |
Dvorkin; Jack (Houston, TX),
Tono; Henrique (Houston, TX), Sisk; Carl (Houston,
TX), Malone; David (Houston, TX) |
Applicant: |
Name |
City |
State |
Country |
Type |
Dvorkin; Jack
Tono; Henrique
Sisk; Carl
Malone; David |
Houston
Houston
Houston
Houston |
TX
TX
TX
TX |
US
US
US
US |
|
|
Assignee: |
Ingrain, Inc. (Houston,
TX)
|
Family
ID: |
57351965 |
Appl.
No.: |
13/465,105 |
Filed: |
May 7, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
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61484254 |
May 10, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01V
5/101 (20130101); G01V 5/04 (20130101); G01N
23/046 (20130101); G01N 23/2251 (20130101); G01N
33/241 (20130101); G01N 2223/616 (20130101); G01V
5/104 (20130101); G01N 2223/419 (20130101); G01V
5/107 (20130101) |
Current International
Class: |
G01V
5/10 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Rothman, D., et al., Lattice-Gas Cellular Automata. Cambridge, UK:
Cambridge University Press, 1997, pp. 155-157. cited by
applicant.
|
Primary Examiner: Vargas; Manuel Rivera
Attorney, Agent or Firm: Kilyk & Bowersox, P.L.L.C.
Parent Case Text
This application claims the benefit under 35 U.S.C. .sctn.119(e) of
prior U.S. Provisional Patent Application No. 61/484,254, filed May
10, 2011, which is incorporated in its entirety by reference
herein.
Claims
What is claimed is:
1. A method for making estimates of subterranean rock properties,
comprising: a) positioning a logging tool inside a well bore, c)
measuring in situ well properties in the well interval in the well
using the logging tool, d) estimating rock properties in the well
for a location of the logging tool in the well interval using the
measured in situ well properties, e) retrieving at least one rock
sample from the well interval in the well, f) preparing said at
least one rock sample for digital rock physics analysis, g)
scanning the at least one rock sample to produce a digital image of
said rock sample, h) segmenting said digital image of said rock
sample to define pores and grains in said digital image, i)
adjusting said digital image to represent said rock properties at
in-situ conditions using said well properties, j) calculating rock
properties from said adjusted digital image of said rock sample
using said in situ fluid properties, k) comparing said rock
properties at the well interval where the logging tool was
positioned and said rock properties from said digital image of said
rock sample using said in situ fluid properties, l) positioning a
logging tool inside a well bore of a similar formation as that of
the wellbore of step a), m) repeating step c) for measuring in situ
well properties using the logging tool used in step l), n)
estimating rock properties in the well for a location of the
logging tool used in step l) in a well interval using the measured
in situ well properties from step m), and o) correcting the
estimates of rock properties from step n) during the logging
process using rock properties or rock property trends determined
from said adjusted digital image in step j).
2. The method of claim 1, further comprising: conducting an in situ
pressure transient test during the well interval.
3. The method of claim 2, further comprising: a digital simulation
of said in situ pressure transient test.
4. The method of claim 2, further comprising: a comparison of said
in situ pressure transient test and said digital simulation of said
in situ pressure transient test.
5. The method of claim 1, further comprising: l) at least one of
storing, displaying, and printing results of said comparing.
6. The method of claim 5, further comprising: m) extracting at
least one of fluid and gaseous contents of a subsurface reservoir
at or adjacent said well interval based on results of said
comparing.
7. The method of claim 1, wherein said rock sample is produced by
rotary core, percussion, or combinations thereof.
8. The method of claim 1, wherein said in situ well properties are
downhole images, well bore gauge, temperature, pressure,
resistivity, gamma, neutron-density, T1 and T2 relaxation times
from NMR, or any combination thereof.
9. The method of claim 1, wherein said rock properties comprise
absolute permeability, total porosity, connected porosity, relative
permeability, capillary pressure, m and n Archies constants,
elastic moduli, or electrical properties, or any combinations
thereof.
10. The method of claim 1, wherein said rock sample has a diameter
size of about 2 cm or less and a length of about 2 cm or less.
11. The method of claim 1, wherein step k) comprises comparing (i)
a logging tool data point plotted as absolute permeability versus
porosity for the rock properties estimated from the in situ well
properties measured with the logging tool in the well interval,
with (ii) digital rock physics data points plotted as absolute
permeability versus porosity for the rock properties calculated
from said adjusted digital images, and selecting the digital rock
physics data point which is closest to the logging tool data point,
and further comprising using the selected digital rock physics
point in at least one subsequent digital rock physics
calculation.
12. A computer program product on a non-transitory computer
readable medium that, when performed on a processor in a
computerized device provides a method for performing computations
of steps (h), (i), (j), (k), and (o) of claim 1.
13. A method for estimating subterranean rock properties of a rock
formation comprising: a) positioning a logging tool at more than
one location inside a well bore, b) measuring in situ fluid
properties in a well using a logging tool at said more than one
location, wherein said in situ fluid properties comprise
temperature, pressure, viscosity, chemical composition, fluid
compressibility, or density, or any combinations thereof, c)
measuring in situ well properties at said more than one location,
d) estimating rock properties in the well at said more than one
location, e) retrieving rock samples from approximately each of
said more than one locations, f) preparing said rock samples for
digital rock physics analysis, g) scanning said rock samples to
produce digital images of said rock samples, h) subdividing said
digital images into 8 or more digital sub-images, i) segmenting
said digital sub-images to define pores and grains in said digital
sub-images, j) adjusting said digital sub-images to represent said
rock properties at in situ conditions using said well properties
and calculating rock properties from said adjusted digital
sub-images of said rock sample using said in situ fluid properties,
k) establishing rock physics relations comprising trends,
transforms, models, or any combinations thereof, l) expanding said
rock physics relations beyond the range of parameters measured in
said trends, transforms, models or any combinations thereof, m)
comparing said rock properties in the well approximately at the
more than one location of the logging tool and said rock properties
from said adjusted digital sub-images of said rock sample using
said in situ fluid properties, n) selecting one or more rock
properties measured in the well or core sample, o) reconstructing
additional rock properties by applying said rock physics relations
trends, transforms and models to said one or more rock properties
measured in the well or core sample, p) positioning a logging tool
inside a well bore of a similar formation as that of the wellbore
of step a), q) repeating step c) for measuring in situ well
properties using the logging tool used in step p), r) estimating
rock properties in the well for a location of the logging tool used
in step p) in a well interval using the measured in situ well
properties from step q), and s) correcting the estimates of rock
properties from step r) during the logging process using rock
properties or rock property trends determined from said adjusted
digital image in step j).
14. The method of claim 13, wherein said rock sample is produced by
rotary core or percussion.
15. The method of claim 13, wherein said in situ well properties
comprise downhole images, well bore gauge, temperature, pressure,
resistivity, gamma, neutron-density, T1 and T2 relaxation times
from NMR, or any combinations thereof.
16. The method of claim 13, wherein said rock properties comprise
absolute permeability, total porosity, connected porosity, relative
permeability, capillary pressure, m and n Archies constants,
elastic moduli, electrical properties, or any combinations
thereof.
17. The method of claim 13, wherein said rock physics relations
comprise at least one relationship between velocity, porosity, and
mineralogy; permeability, porosity, and mineralogy; electrical
formation factor, porosity and mineralogy; and relative
permeability and saturation.
18. The method of claim 13, wherein said rock sample has a diameter
size of about 2 cm or less or a length of about 2 cm or less.
19. A system for making estimates of subterranean rock properties,
comprising: a) a logging tool positionable inside well boreholes of
similar formation and operable for measuring in situ fluid
properties and well properties in at least one well interval in a
well, wherein said in situ fluid properties comprise temperature,
pressure, viscosity, chemical composition, fluid compressibility,
or density, or any combinations thereof, b) at least one computer
processor programmable for estimating rock properties in the well
for a location of the logging tool in the well interval using the
measured in situ well properties, c) a sampling tool operable for
retrieving at least one rock sample from a rock formation bounding
the well borehole in the well interval, and d) a (computer
tomographic) CT or (scanning electron microscope) SEM scanner
operable to produce digital images of at least one retrieved rock
sample from the formation, and e) at least one computer processor
programmable for segmenting said digital images of said rock sample
to define pores and grains in said digital image, adjusting said
digital image to represent said rock properties at in-situ
conditions using said well properties, calculating rock properties
from said adjusted digital image of said rock sample using said in
situ fluid properties, comparing said rock properties at the well
interval where the logging tool being positionable and said rock
properties from said adjusted digital image of said rock sample
using said in situ fluid properties, and comparing additional rock
properties at a well interval where a logging tool being
positionable in a similar formation and said rock properties from
said adjusted digital image of said rock sample using said in situ
fluid properties for correcting the additional rock properties.
20. The system of claim 19, wherein the logging tool comprises a
modular formation dynamics tester (MDT) tool and the sampling tool
comprises a sidewall rotary coring tool.
Description
BACKGROUND OF THE INVENTION
The present invention relates to methods and systems for
integrating logging tool data and digital rock physics to estimate
rock formation properties. More particularly, the present invention
relates to estimating rock formation properties with integration of
logging tool data obtained from a subsurface rock formation and
digital rock physics using digital computer tomographic (CT) and/or
scanning electron microscope (SEM) images of rock samples retrieved
from the same interval of the formation.
Exhaustive formation characterization from remote measurements that
include seismic reflection profiling, well logging, and well
testing requires a set of controlled laboratory experiments
conducted on rock samples that represent the formation under
examination. Even if the bulk density, elastic-wave velocity, and
mineralogy are directly measured in the well, the permeability
(especially relative permeability) and capillary pressure curves
are not. Even if well tests are conducted, the permeability cannot
be directly derived simply because the formation response to
pressure and fluid flux variations that include, permeability, and
a number of other quantities, such as porosity, fluid and formation
compressibility, and reservoir geometry. These tests cannot replace
controlled laboratory measurements where the absolute and relative
permeability are measured on a set of samples covering ranges of
porosity and mineralogy and at varying fluid content inside the
pores space.
Typically, relations between the elastic properties and porosity
and mineralogy are established in the laboratory, generalized by
rock physics theory, and then applied to seismic data, which
reflect the elastic properties of the subsurface, to infer the as
yet unknown porosity in the remotely sensed formation. The same
principle is true for permeability: a relation between permeability
and porosity and lithology is established in the laboratory and
then applied to appropriate well log curves (density, neutron
porosity, NMR) to infer the permeability in the logged
interval.
The problem is that in order to conduct controlled experiments in
the physical laboratory to cover a relevant range of rock property
variation, a fairly large set (>20) of well preserved and
regularly shaped plugs at least an inch in length and diameter is
required. Even if such plugs are available, conducting special core
analysis (SCAL) for relative permeability and capillary pressure is
extremely difficult and requires a long time, from weeks to
months.
Methods and apparatus for subterranean formation flow imaging have
been disclosed, e.g., in U.S. Pat. No. 6,856,132. There are four
broad categories of estimation techniques which have been used in
the oil and gas industries: logging tools, physical laboratory
experiments, pressure transient tests/analysis, and digital rock
physics. Logging tools and digital rock physics are described
below.
Logging instruments, such as the Modular Formation Dynamics Tester
manufactured by Schlumberger Oilfield Services, have been used to
measure the formation and fluid properties in-situ. The
temperature, pressure, composition, capillary tension, and
viscosity of oil, gas and water and mixtures thereof are inputs to
calculating relative permeability. In addition to fluid properties,
it is necessary to know the petrophysical parameters of a
geological formation such as fluid saturation, the porosity of the
formation and its permeability. Formation porosity is the pore
volume per unit volume of formation; it is the fraction of the
total volume of a rock sample that is occupied by pores or voids.
The saturation of a formation is the fraction of its pore volume
that is occupied by the fluid of interest. Thus, water saturation
is the fraction of the pore volume that contains water. The water
saturation of the formation can vary from 100 percent to a small
value that cannot be displaced by oil, and is referred to as the
irreducible water saturation. In most cases it is assumed that the
hydrocarbon saturation of the formation is equal to one minus the
water saturation. Obviously, if the formation's pore space is
completely filled with water, such a formation will not produce oil
or gas and is of no interest. Conversely, if the formation is at an
irreducible water saturation, it will produce all hydrocarbons and
no water. Finally, the permeability of a formation is a measure of
the ease with which fluids can flow through the formation, i.e.,
its producibility.
Traditional methods for measuring these producibility parameters
involve wireline logging or logging while drilling (LWD) techniques
that generally include resistivity, gamma, and neutron-density
measurements, commonly known as the "triple-combo." For wireline
measurement, a tool is lowered below the zone of interest on an
armored multiconductor cable that provides power and
communications. The tool is then moved up and down through the
borehole making measurements along the way. In the instance of LWD,
the measurements are made while drilling is taking place. In this
case the tool is mounted on specialized fixtures in the drilling
string. Each of these methods has advantages and disadvantages. The
wireline method is generally capable of providing a more accurate
measurement than LWD, and the data is acquired in real time. The
LWD method is susceptible to effects such as tool position within
the borehole and making the measurements in a relatively new
borehole prior to drilling fluids entering the formation. The
triple combo measurements are subject to a number of effects from
the borehole environment. Resistivity tools respond to conductive
fluids, including moveable water, clay bound water, capillary bound
water and irreducible water. A number of models have been developed
to estimate the water saturation of a formation. However, the
recognition of pay zones within a rock formation is difficult
because the conductivity difference between capillary-bound water
and displaceable water cannot be measured. In addition, resistivity
measurements are subject to borehole rugosity and mudcake effects.
Similarly, neutron-density measurements respond to all components
within the formation but are more sensitive to the formation matrix
as opposed to the fluids contained therein. Even after cross plot
corrections, borehole rugosity, mudcake, lithology and other
environmental effects can adversely effect the measurement.
Nuclear magnetic resonance (NMR) logging is a relatively recent
commercial method employed in wireline logging to estimate
formation parameters and other parameters of interest, for a
geological formation. Unlike nuclear porosity logs, which utilize
isotopic radioactive sources, the NMR measurement is
environmentally safe and is less affected by variations in matrix
lithology than most other logging tools. NMR logging is based on an
assembly of magnetic moments, each having a certain angular
momentum. When exposed to a static magnetic field they tend to
align at a certain angle to the direction of the magnetic field,
and will process with the Larmor frequency around the direction of
the magnetic field. The rate at which equilibrium is established
upon provision of a static magnetic field is characterized by the
parameter T.sub.1, known as the spin-lattice relaxation time.
Another related NMR parameter is the spin-spin relaxation time
constant T.sub.2 (also known as transverse relaxation time) which
is an expression of the relaxation due to dynamic non-homogeneities
on molecular length scales.
The MRIL.RTM. tool manufactured and utilized by the NUMAR product
service line of Halliburton Energy Services and the CMR.TM. tool
manufactured and utilized by Schlumberger Oilfield Services
represent recent developments in the field of NMR logging and are
both suitable for inferring porosity, permeability, and fluid
type.
Using T.sub.1 and/or T.sub.2 relaxation times, one can determine a
number of formation properties. Porosity can be estimated by means
of signal intensity. Fluid typing utilizes T.sub.1, T.sub.2 and/or
diffusion measurements and is usually based on the geometry and
sizes of the pores, as well as by the viscosity of the fluid being
measured. The bulk volume index (BVI) and free fluid index (FFI)
are measured based on T.sub.2 and empirically derived formulas. The
formation permeability estimates are based on T.sub.1 and/or
T.sub.2 measurements and one of several empirically derived
models.
With respect to permeability, several models have been used to
estimate formation permeability. The first method is based on
T.sub.1 and/or T.sub.2, porosity and is estimated by various
oilfield service and oil exploration companies according to
equations 1-3 below: k.apprxeq..phi..sup.4T.sub.1.sup.2 [1]
k=C.phi..sup.4T.sub.2ML.sup.2 [2]
k.apprxeq..phi..sup.2T.sub.1.sup.2 [3] where k is permeability,
.phi. is porosity, C is an empirically derived constant and
T.sub.2ML is the logarithmic mean of the T.sub.2 distribution.
Yet another model estimates formation permeability based on the
bound water information (often referred to as the Coates model)
according to equation 4 below:
.apprxeq..PHI..times. ##EQU00001## where FFI is the free fluid
index, which is determined by partitioning the total measured NMR
response by the T.sub.2cutoff, which is the value of T.sub.2 that
is empirically related to the capillary properties of the wetting
fluid for the specific formation lithology. The porosity estimate
below T.sub.2cutoff is generally referred to as the bound fluid
porosity or bulk volume irreducible (BVI). While estimates of
T.sub.2cutoff values have been made for various types of
mineralogy, the only accurate means of determining T.sub.2cutoff is
by performing NMR measurements on a core sample.
Another model for estimating formation permeability is based on the
restricted diffusion and pore size of the formation as set forth in
equation 5 below:
k.apprxeq..phi..sup.3/((1-.phi.).sup.2.tau.(S/V).sup.2) [5] where
S/V is the pore surface to volume ratio and .tau. is the rock
tortuosity.
Each of the above models has drawbacks in their application. For
instance, equation 4 (the Coates model) may not be valid if gas is
present in the sample or if the estimate of the T.sub.2cutoff is
significantly in error. The Kozeny-Carman model set forth in
equation 5 was derived for an artificial pore-space geometry
(parallel pipes) and has to be adjusted over wide range of
reservoir lithology, including such parameters as grain size
distribution and pore shape.
Other logging techniques have been used to estimate formation
permeability. Primary among them is the use of formation test tools
to determine formation permeability. A formation test tool is
lowered into the borehole and brought into contact with the
formation wall. A probe is inserted past the mud cake to come in
contact with the formation itself. Fluid is then withdrawn from the
formation using a pre-charge piston or pumping means. This
"pressure draw down" period induces fluid into the tool that may be
diverted to sampling chambers or, ultimately, discharged back into
the borehole. Following the pressure draw down, formation pressure
can be measured as the pressure returns to the natural formation
pressure. There are a number of models for estimating permeability
based on the formation pressure and temperature tool data. These
models may include a laminar or spherical model design. The use of
formation testers to determine permeability is known, and U.S. Pat.
Nos. 6,047,239, 5,247,830, and 4,745,802, for example, set forth
exemplary formation test tools. As noted previously, these
formation test evaluation techniques pre-suppose the use of a
particular model, which in turn pre-supposes the nature of the
formation itself. The formation may be thinly laminated near the
test point or have a large, consistent lithology. It is well known
that models designed to work in a consistent lithology will not
yield an accurate result where the formation is thinly laminated
with the layers each having differing porosity and permeability
characteristics. Formation test tools are generally incapable of
measuring anisotropic permeability, i.e., vertical versus
horizontal permeability. An additional downside to using formation
test tools is the fact that logging tool movement must be stopped
to permit the formation test tool to come into contact with the
formation, perform the draw down and permit the pressure to build
back up. It may require several minutes to hours to perform the
draw down and build up. In this case, prior to wireline logging
operations, the drill string must be "tripped" or removed from the
borehole to permit logging. This results in costs in addition to
the cost of services associated with logging. The "triple combo
test" and NMR logging tools noted above are used in continuous
logging operations, that is, the measurements are made as the tool
is moved up or down the borehole at rates exceeding three feet per
minute. Modern borehole logging speeds typically exceed 30 feet per
minute. Thus, while providing some information regarding
permeability, formation test tools are costly to use when compared
to NMR logging tools. At the same time, NMR logging tools make
certain assumptions regarding permeability that may not be accurate
in light of actual formation conditions.
Recently, some efforts have been made to combine NMR techniques
with formation test tools. Halliburton, Schlumberger and Baker
Atlas have introduced techniques in which fluid identification is
performed on the fluid withdrawn from the formation during one of
the formation tests. Examples on these types of techniques are set
forth in U.S. Pat. Nos. 6,111,408 and 6,111,409. In each instance,
the NMR experiment is performed on the fluid that is no longer in
situ. As a result, the fluid may undergo a phase change when
removed from the downhole environment.
Downhole Fluid Analysis (DFA) typically is necessary to calculate
formation properties such as absolute permeability, relative
permeability, and capillary pressure. Typical logging techniques
extract a sample from the well bore and transport it to the surface
for analysis. Tests used to quantify fluid composition are
typically chromatographic methods. These lab tests are subject to
error due to transport and changes of the material properties that
may occur in replicating downhole conditions. In addition, it is
well known that the fluid in a well or formation is not homogeneous
and the fluid properties vary in both time and depth location.
Logging tool samples are small and represent a very small
percentage of the overall fluid in the well. Multiple samples can
be taken but this requires significant time and expense. Recently,
some companies such as Schlumberger, have developed in-situ
analysis techniques based on spectroscopy. Schlumberger's MDT
family of tests includes In-Situ Family (density, composition,
gas-oil ratio, CO.sub.2, pH, fluorescence, color and fluid
profile), Composition Fluid Analyzer (C.sub.1, C.sub.2-C.sub.5,
C.sub.6+, H.sub.2O, CO.sub.2), Live Fluid Analyzer (analyzes fluids
as they flow through the MDT), MDT Permeability (k.sub.v and
k.sub.h estimates are usually based on 1000 cm.sup.3 samples of
reservoir fluid), MDT Single Phase (PVT-quality single-phase fluid
sample removed from the reservoir), MDT Low Shock Sampling (limits
pressure drawdown during fluid sampling), and Combinable Magnetic
Resonance (estimate the distribution of pore sizes in the formation
and identify hydrocarbons in low-contrast, low-resistivity pay
zones with high-resolution NMR). The availability of in-situ fluid
composition techniques has made it possible to estimate formation
properties such as relative permeability. The estimation of
relative permeability in this manner is indirect. The detailed
structure of the formation pore structure typically is not
determined and known by sensing done with a conventional logging
tool. Many factors such as contamination of fluids with drilling
mud and non-representative sample locations can introduce
significant errors in the logging tool estimates of relative
permeability. These errors are magnified in tight formations such
as carbonates and shale or tight-gas sandstone.
The other broad technique for formation analysis, digital rock
physics, utilizes rock samples withdrawn from the formation and
evaluated in the computational rock physics laboratory. The rock
sample may be from a core sample, drill cuttings or other suitable
means. Samples are selected to be as representative of the
formation as possible. CT scan imaging of a sample of rock
formation is used to produce a numerical object that represents the
material sample digitally in the computer. The raw CT scan data is
further processed or segmented to produce an accurate 3D digital
representation of the selected sample. Subsequent numerical
simulations of various physical processes include viscous fluid
flow (for permeability estimation); two phase fluid flow (for
relative permeability estimation); stress loading (for the
effective elastic moduli); electrical current flow (for
resistivity); and pore size distribution for nuclear magnetic
resonance relaxation time properties, including distributions of
the relaxation time.
A rock sample is placed inside a CT-scan machine where it is
illuminated with focused X-rays of desired frequency. This
frequency determines the resolution of the image--the size of a
single voxel can vary from a few nanometers to a micron and to a
centimeter. The sample is mechanically rotated inside the machine
to view it at all angles. The software supplied with the machine
tomographically reconstructs the 3D volume. These images come in
shades of gray. The gray level is directly affected by the average
atomic number of the material, which is, simply speaking, its
average density. For example, if the rock fragment under
examination contains dolomite, calcite, quartz, porous clay, and
air in the large pores, the brightness of the voxels representing
these entities will reduce from almost white for dolomite to almost
black for the pores. The porous clay will appear as a darker gray
because (depending on its intrinsic porosity) its bulk density is
smaller than that of the pure clay mineral. This method may not
necessarily resolve the individual clay particles, but it will
identify the clay and can estimate its porosity by assuming the
density of the clay mineral that is related to the clay type. The
same holds for porous micrite in carbonates. If the pores contain
fluids with a significant density contrast (e.g., water and air),
the fluid phases can be identified in the CT image.
A nano-CT machine can resolve small micritic grains. It can also
resolve relatively large shale particles but not the smallest clay
platelets. To image the latter, FIB-SEM (focused ion beam combined
with scanning electronic microscope) technique is commonly used.
The ion beam removes the rock material above the cutting plane and
exposes a flat unaltered area, which is then imaged by SEM. Such 2D
images can be obtained sequentially at extremely close planes. Then
these 3D images are combined to produce a tomographic 3D image at a
very high (5-10 nm) resolution. The entire gray-scale range of the
image can be reduced to a few integers, such as 0s for pores, 1s
for quartz, and 2s for calcite. This procedure is based on fairly
sophisticated image-processing algorithms (rather than simple
intensity/color thresholding) and is essential for any process
simulation as it strictly defines the pore space where the fluid
flows and the mineral phases through which the elastic stress is
transmitted. FIGS. 1A, 1B, and 1C display a segmented image, 0s
(black) for the pores and 1s (gray) for calcite with the same
sample displayed with increasing magnification from left to right
in these figures. The smaller features of the pore space become
apparent as magnification increases. After a digital image is
acquired and segmented, fluid flow can be simulated in the digital
pore space and relative permeability curves are computed, such as
illustrated in FIGS. 2A-2B (black dots=gas, grey dots=water). In
FIGS. 2A and 2B, relative permeability curves for water and gas in
the same sandstone sample are shown, with varying interfacial
tension (from left to right in these figures). A salient feature of
such calculations is that the pore space and mineral matrix of the
segmented image are not replaced by an idealized geometry (as in,
e.g., network modeling). Rather, the image is used directly, with
all its visible intrinsic complexity intact.
In characterizing formation characteristics, absolute permeability
k.sub.Absolute is often defined from Darcy's equation
.function..mu..times.dd ##EQU00002## where Q is the volume flux
through the sample (m.sup.3/s); A is the cross-sectional area of
the sample (m.sup.2); .mu. is the dynamic viscosity of the fluid
(Pas with 1 cPs=10-3 Pas); and dP/dx is the pressure drop across
the sample divided by the length of the sample (Palm).
Theoretically, the absolute permeability depends only on the
pore-space geometry but not on the pore fluid. A simple and
powerful equation to estimate k.sub.Absolute is the Mavko-Nur
(1997) modification of the Kozeny-Carman equation:
.times..tau..times..PHI..PHI..PHI..PHI. ##EQU00003## where
d.sub.Mean is the mean grain size; .tau. is the tortuosity; o is
the total porosity; and o.sub.p is the percolation porosity
(porosity at which the pore space becomes disconnected and, hence,
permeability becomes zero). Permeability has the same units as
d.sup.2.sub.Mean.
Timur introduced one of the most commonly used equations that link
the absolute permeability to porosity o and irreducible water
saturation S.sub.wi:
k.sub.Absolute=8581.phi..sup.4.4/s.sup.2.sub.wi [8] where S.sub.wi
are in unitless volume fractions and k.sub.Absolute is in mD.
Equation 8 is used to calculate the irreducible water saturation
from the permeability (in mD).
All of these equations have been verified by and calibrated to a
finite number of datasets which are often idealized or artificial
(e.g., the Fontainebleau sandstone or glass beads). To become
applicable to the variety of formations encountered in real-world
petroleum exploration, these equations have to be re-adjusted for
each single formations type and these readjustments cannot be known
a-priori.
Absolute permeability varies with porosity and Rothman and Zaleski
(Rothman, D. and Zaleski, S., Lattice-Gas Cellular Automata.
Cambridge, UK: Cambridge University Press, 1997. Pages 155-157)
have studied this variation within a rock sample. They scanned and
segmented a 2 mm.times.2 mm rock sample of Fontainebleau sandstone
and computed porosity and absolute permeability at several scales
covering sizes of 56, 112 and 224 voxels (voxel size of 7.5 .mu.m).
They found that porosity varied over a factor of four for the
smallest to largest samples with corresponding variation in
absolute permeability.
Relative permeability is used to quantify multiphase flow, such as
the flow of oil in the presence of water and water in the presence
of oil. In a sample with two such fluids, the relative
permeabilities k.sub.ro and k.sub.rw, by definition, are
.times..mu..times..times.dd.times..mu..times..times.dd ##EQU00004##
where the subscripts "o" and "w" refer to oil and water,
respectively. The fluxes Q.sub.o and Q.sub.w are measured at fixed
water saturation S.sub.w. Relative permeability is usually plotted
versus Sw. Because these fluxes of the fluid phases at partial
saturation are smaller than the flux measured in a sample fully
saturated with water or oil, the relative permeability is always
smaller than 1 and larger than or equal to 0. Typical k.sub.ro and
k.sub.rw versus S.sub.w curves produced by digital two-phase flow
simulations are displayed in FIG. 3.
The relative permeability depends on more factors than
k.sub.Absolute, including the wettability of the fluids and
minerals system, interfacial surface tension, and viscosity
contrast between the fluid phases. These parameters may vary in
space and time, the latter due to pressure, flow, and the resulting
hydrocarbon state and composition changes during production.
To estimate permeability for single as well as two-phase fluid
flow, the lattice-Boltzmann computational method (LBM) can be used
to solve the Navier-Stokes Equations. LBM is based on Newtonian
dynamics of particles traveling and colliding on a 3D spatial grid.
With the collision rules appropriately specified, the particle
speed and pressure fields precisely mimic those governed by the
Navier-Stokes equations for viscous flow. The importance of LBM for
fluid-flow simulation is that the no-slip boundary conditions can
be implemented at the fluid-solid boundaries of any geometry (as
imaged), which is essential in real pore space. For a multiphase
flow, the wettability angles, interfacial tension, and viscosities
are specified prior to computation. The elastic properties are
simulated using a finite-element method (FEM) with the elements
placed in the mineral matrix and their elastic moduli assigned
according to the mineral types as determined during segmentation.
This is not a purely mathematical procedure as it requires a
decision by a geologist and petrophysicist to select minerals
appropriate to the rock under examination. Sometimes selected SEM
images are taken to better understand and identify the rock.
The electrical conductivity is also computed using SEM with the
elements placed in the pore space and conductive minerals. Similar
to other simulations, the conductivities of the individual phases
are specified prior to calculations.
Because these computational experiments are conducted on the same
digital object and in precisely imaged pore space, the named
attributes of rock can be interrelated.
Some advantages of logging techniques per se can include, for
example, the following.
1. The ability to directly sample formation fluids at in-situ
conditions and calculate their chemical composition and mechanical
properties.
2. Logging techniques combined with well tests also provide a
series of well tests that estimate reservoir flow properties at the
near-wellbore as well as in the far field. These property estimates
can be provided in the horizontal and vertical directions thus
estimating flow-property anisotropy.
3. Log data of GR, density, neutron porosity, and NMR can be used
to estimate the mineralogy, porosity, and the pore-size
distribution.
Some disadvantages of logging techniques per se can include, for
example, the following.
1. Flow properties provided do not directly calculate permeability.
Rather these direct measurements reveal the diffusivity, a
combination of permeability, porosity, fluid compressibility, and
matrix compressibility. The assumed reservoir geometry is also a
key input into inferring permeability from well tests. As a result,
the estimates of permeability and relative permeability are subject
to significant error and variability. These errors may differ
significantly depending upon the type of rock formation being
evaluated.
2. The flow properties provided by logging tools are snap-shots in
time and space. As such they lack forecasting power to predict how
the reservoir behaves at different stages of development as fluid
saturations and fluid properties change versus pressure and
temperature.
3. Mineralogy, porosity, and the pore-size distributions from
logging tools are indirect measurements. They are interpretations
of the responses of the formation to different excitations and are
therefore only indirectly related to the parameters of direct
interest.
4. Fluid samples taken by logging tools are small and may not
represent the fluid properties of the entire formation. Fluid
properties are not the same at all locations within a
formation.
5. Fluid samples taken with logging tools may be contaminated with
drilling fluid when the samples are taken on a newly drilled
well.
6. Fluid samples taken by logging tools depend upon a seal of the
sampling tool at the surface of the well bore. The irregularity of
the surface at the well bore can interfere with this seal. In
addition, tight formations such as carbonates or shales require
high pressures to inject or withdraw formation fluid samples. In
these cases a tight seal is very difficult to achieve.
Some advantages of digital rock physics techniques per se can
include, for example, the following.
1. A mathematical model of the intimate texture of rock (e.g.,
whether it is cemented or loose) as well as the position of various
minerals relative to the pores space (e.g., grain-coating kaolinite
of pore-obstructing illite, etc) provide a very accurate
representation of rock structure.
2. Direct simulation of processes inside the digital sample can
deliver all rock properties from the same rock object.
3. Digital rock physics provides the ability to vary at will the
conditions of the computational experiment thus covering rock
response at any foreseeable conditions during the life of the
reservoir, including depletion, injection, as well as thermal
treatment.
4. From very small fragments of rock, digital rock physics can
deduce relations between pairs of rock properties, such as
permeability versus porosity, formation factor versus porosity,
elastic properties versus porosity, and so forth.
Some disadvantages of digital rock physics per se can include, for
example, the following.
1. Samples typically range in size from a few millimeters to a few
centimeters in size. Such samples may not be perceived as
representative of the entire well or formation.
2. The digital images of rock samples are derived from samples
scanned at the surface of the earth. Methods have been developed to
correlate rock properties at earth surface conditions with rock
properties at in-situ conditions. Reliance on such correlations is
perceived as risky or error prone by some well operators.
3. In order to compute rock properties such as relative
permeability and capillary pressure that are dependant on rock and
fluid conditions, digital rock physics must make estimates of the
fluid densities, wettability, and viscosities to be used in
computations. There is no way to concretely verify these
estimates.
The present investigators have recognized a need in the industry
for integration of well logging and digital rock physics
technologies to yield unique rock formation evaluation capabilities
and enhanced formation models.
SUMMARY OF THE INVENTION
A feature of the present invention is obtaining a rock sample from
a logging tool such as a sidewall plug or large enough cutting
extracted by the logging tool in the same well interval that the
logging tool measures fluid properties. The rock samples thus
obtained are scanned using a computer tomographic (CT) scanner,
scanning electron microscope (SEM), or other suitable scanning
device. The resulting scanned rock image is segmented and rock
properties comprising porosity, absolute permeability, relative
permeability, capillary pressure, and/or other relevant rock
properties are calculated. The integration of enhanced digital rock
physics techniques with downhole well logging according to the
present invention can correct and/or support upscaling of the well
logging results relative to the rock formation of interest.
To achieve these and other advantages and in accordance with the
purposes of the present invention, as embodied and broadly
described herein, the present invention relates to using porosity,
absolute permeability, relative permeability, and/or capillary
pressure curves computed from digital rock physics along with the
in-situ temperature, pressure, composition and viscosity of the
mixtures of oil, gas, and water, NMR data, and/or estimates of
relative permeability from logging tools, to generate absolute
permeability versus porosity trends at in-situ conditions that are
used to expand the datum obtained from logging tools into a
permeability-porosity transform accounting for the natural porosity
variations within the reservoir sampled by the logging tool.
Porosity, absolute permeability, relative permeability, and/or
capillary pressure curves computed from digital rock physics along
with the in-situ temperature, pressure, composition, and viscosity
of the mixtures of oil, gas, and water, NMR data, and estimates of
relative permeability from logging tools can be used to generate
elastic properties versus porosity and mineralogy with in-situ
fluids present in the pores and for a range of in-situ fluid
saturations.
Porosity, absolute permeability, relative permeability, and/or
capillary pressure curves computed from digital rock physics along
with the in-situ temperature, pressure, composition, and viscosity
of the mixtures of oil, gas, and water, NMR data, and estimates of
relative permeability from logging tools can be used to generate an
electrical formation factor and the m and n Archie's constants
properties versus porosity and mineralogy with in-situ fluids
present in the pores and for a range of in-situ fluid
saturations.
Porosity, absolute permeability, relative permeability, and/or
capillary pressure curves computed from digital rock physics along
with the in-situ temperature, pressure, composition, and viscosity
of the mixtures of oil, gas, and water, NMR data, and estimates of
relative permeability from logging tools can be used to generate
relative permeability versus porosity curves for the in-situ fluid
properties varying in ranges plausible to encounter during the
life-time conditions of the reservoir.
Porosity, absolute permeability, relative permeability, and/or
capillary pressure curves computed from digital rock physics along
with the in-situ temperature, pressure, composition, and viscosity
of the mixtures of oil, gas and water, NMR data, and estimates of
relative permeability from logging tools can be used to generate
wettability versus porosity curves for the in-situ fluid properties
varying in ranges plausible to encounter during the life-time
conditions of the reservoir.
Porosity, absolute permeability, relative permeability, and/or
capillary pressure curves computed from digital rock physics along
with the in-situ temperature, pressure, composition, and viscosity
of the mixtures of oil, gas and water, NMR data, and estimates of
relative permeability from logging tools can be used to generate
capillary pressure versus saturation and porosity curves for the
in-situ fluid properties varying in ranges plausible to encounter
during the life-time conditions of the reservoir.
Porosity, absolute permeability, relative permeability, and/or
capillary pressure curves computed from digital rock physics along
with the in-situ temperature, pressure, composition and viscosity
of the mixtures of oil, gas and water, NMR data and estimates of
relative permeability from logging tools can be used to create a
database of digital rock physics trends for various formation types
and to correlate these with measurements from logging tools such
that corrections can be made to estimates from logging tools for
the complexity of actual rock pore structures.
A further feature of the present invention is verification and
calibration of logging tool estimates of rock properties by
contrasting them to the properties computed on rock samples using
digital rock physics extracted.
A further feature of the present invention is use of digital rock
physics to enrich logging tool results by providing insights into
the pore-scale rock structure.
A further feature of the present invention is use of a digital rock
sample analogue in lieu of or in conjunction with an extracted rock
sample and in-situ the fluid properties produced by the logging
tool to compute improved rock properties.
While the inventors have found that trends produced as described in
the present invention can represent the range of rock properties in
a facies, well, or formation, it remains important to select those
rock properties which are most representative of the facies, well,
or formation. The present invention also includes a method to
select one or more digital sub-samples that are most representative
of the facies, well or formation.
A further feature of the present invention is digital simulation of
logging tool transient pressure tests by using the digital rock
structure obtained from scanning and segmentation of the rock
sample extracted by the logging tool and the downhole fluid
properties from the logging tool. The simulated transient pressure
response and the actual pressure response are correlated to make
corrections to the porosity, permeability and relative permeability
estimated by the logging tool.
BRIEF DESCRIPTION OF DRAWINGS
FIGS. 1A-1C show a segmented image for calcite, with 0s (black) for
the pores and 1s (gray) for the calcite solids. The images of FIGS.
1A-1C are (left to right) about 20, 8, and 2 microns across.
FIGS. 2A-B are plots showing relative permeability curves to water
(grey dots) and gas (darker black dots) in the same sandstone
sample, but with varying interfacial tension (from left to
right).
FIG. 3 is a plot showing relative permeability curves where the
permeability to oil at the irreducible water saturation is about
40% of the absolute permeability. These curves are obtained from
digital rock simulations.
FIG. 4 shows a plot of a quadrant analysis of calculations produced
by digital rock physics and a logging tool used in a method to
upscale rock property results obtained from subsampling a small
rock sample to estimate properties for subterranean facies or an
entire formation according to the present invention.
FIG. 5 is a cross-sectional view of a system for integration of
well logging and in situ condition analysis when an MDT tool is
deployed in a well bore at the well interval or intervals of
interest, and core sample retrieval and 3D scan imaging analysis of
a retrieved rock sample or samples from the same well interval or
intervals, according to the present invention.
FIG. 6 is a further cross-sectional view of the system of FIG. 5
showing the system when a core sample retrieval tool is deployed in
the well interval of the well bore according to the present
invention.
FIG. 7 is a cross-sectional view of an MDT tool of FIG. 5 when
deployed in the well borehole according to the present
invention.
FIG. 8 is a partial fragmentary side view of a sidewall rotary
coring tool used for the sample retrieval tool shown in FIG. 6 when
deployed in the well borehole according to the present
invention.
FIG. 9 is a flow chart of a Point Method for integrating logging
tool and digital rock physics according to the present
invention.
FIG. 10 is a flow chart of a Trend Method for integrating logging
tool and digital rock physics according to the present
invention.
FIGS. 11A-B are magnified sectional views of thin cracks which
appear in a solid matrix of rock sample due to stress reduction
from in-situ to benchtop conditions.
FIGS. 12A-C show plots of P-wave velocity (top) and permeability
(middle) versus confining stress in tight gas sandstone of about
0.05 porosity. Permeability is plotted versus velocity in the FIG.
12C.
FIGS. 13A-B show plots of P-wave velocity (top) versus porosity in
tight gas sandstone at varying stress. These plots use the same
data as used in FIGS. 12A-C.
FIG. 14 is a set of digital rock images referred to in an example
herein of a method of adjusting a 3D matrix to represent in-situ
conditions.
FIGS. 15A-B are plots of P- and S-wave velocity versus porosity as
computed for the sample shown in FIG. 14 (top-left). The curve is
from the stiff-sand model, and the rock is assumed to be pure
quartz.
FIGS. 16A-C are plots of permeability (decimal logarithm) versus
porosity computed in the x, y, and z directions (left to right) for
the same digital sample as used for velocity computations shown in
FIGS. 15A-B. The y-direction permeability is zero.
FIGS. 17A-B are plots of P- and S-wave velocity versus porosity as
computed for all six samples shown in FIG. 14. The values computed
on the original digital sample are squares. The curve is from the
stiff-sand model, and the rock is assumed to be pure quartz. The
arrows show the direction of increasing crack porosity.
FIGS. 18A-B are plots of decimal logarithm of directional
permeability versus the P- (left) and S-wave (right velocity). The
digital data are the same as displayed in FIGS. 17A-B. The
permeability was computed in three directions. These permeability
values are represented by the squares (x), squares (y), and squares
(z).
FIGS. 19A-C are a set of digital rock images showing 2D slices of
the original digital sample (FIG. 19A) and its two alterations with
porosity 0.126 (FIG. 19B) and 0.138 (FIG. 19C), wherein the slices
are taken along the same planes in respective 3D images.
FIGS. 20A-C are similar plots as FIGS. 16A-C but for all six
digital samples displayed in FIG. 14. The arrow shows the direction
of increasing crack porosity. The data points within the in-situ
permeability range are encircled in blue.
FIGS. 21A-B are plots of P- (left) and S-wave velocity (right)
versus porosity in a tight sandstone sample. The gray square is for
the data computed on the original sample. The shaded circles are
the data computed on the eight subsamples of the original sample.
The criterion velocity curve is shown in black.
FIG. 22 is a schematical perspective view of an original digital
sample divided into subsamples (eight).
FIG. 23A is a plot of permeability versus porosity computed on the
original sample (gray square) and its eight subsamples (colored
circles).
FIG. 23B is a plot similar to FIG. 23A but with data displayed for
only three subsamples whose computed P- and S-wave velocity lie
close to the criterion curve in FIGS. 21A-B.
FIG. 23C is a plot similar to FIG. 23B but with decimal logarithm
of porosity on the horizontal axis. The black square is the
permeability of the original sample adjusted for the in-situ
stress, and the line is the best linear fit to the three subsample
data points.
FIG. 24 is a function model of a system and method for integrating
well log data and digital rock physics to estimate rock formation
properties in accordance with the present invention.
FIG. 25 is a plot of absolute permeability (k.sub.A) versus
porosity (.phi.) for several digital sub-samples and a
corresponding absolute permeability-porosity measure from a logging
tool.
DETAILED DESCRIPTION OF THE PRESENT INVENTION
The present invention relates in part to integrating logging tool
data and digital rock physics in unique ways to estimate rock
formation properties more accurately. The present methods can at
least partly resolve or remedy shortcomings of either a logging
tool or digital rock physic evaluation strategy if used alone. The
present methods and systems, for example, can reduce or eliminate
needs for conducting time-consuming laboratory analyses outside the
well to analyze properties of a rock sample for use in estimating
permeability, porosity, or other rock properties, while taking
advantage of real time in-situ fluid and well property measurement
capabilities of a logging tool. The present invention further
relates in part to estimating rock formation properties with
integration of logging tool data obtained from a subsurface rock
formation and digital rock physics using digital computer
tomographic (CT) and/or scanning electron microscope (SEM) images
of rock samples retrieved from the same interval of the formation.
For purposes of the detailed description herein, references to "CT
scanning" and similar wording alone as used for convenience, may
alternatively or additionally encompass SEM scanning unless
indicated otherwise.
In a method for making estimates of subterranean rock properties,
for example, a logging tool is positioned inside a well bore,
wherein in situ fluid and/or well properties in the well are
measured using the logging tool. These measurements can be made in
a single well interval in the well, or multiple intervals of
interest in the well. For purposes herein, "well interval" refers
to a well location, such as a depth range in the well or formation
in which the well is drilled. Rock properties in the well can be
estimated for a location of the logging tool using the measured in
situ well properties. At least one rock sample is retrieved from
the well. The retrieved rock sample or samples are prepared for
digital rock physics analysis, and then scanned to produce a
digital image of the rock sample. The digital image of the rock
sample is segmented to define pores and grains in the digital
image, and then the digital image is adjusted to represent the rock
properties at in-situ conditions using the well properties. Rock
properties are calculated from the adjusted digital image of the
rock sample using the in situ fluid properties. The rock properties
in the well derived from the logging tool measurements are compared
with the rock properties derived from the digital image of the rock
sample or samples using the in situ fluid properties. The in-situ
fluid properties used in this method can be, for example,
temperature, pressure, viscosity, and/or chemical composition, or
any combinations thereof. The in-situ well properties used in this
method can be, for example, downhole images, well bore gauge,
temperature, pressure, resistivity, gamma, neutron-density, and/or
T.sub.1 and T.sub.2 relaxation times from NMR, or any combinations
thereof. The rock properties calculated and compared in this method
can be, for example, absolute permeability, total porosity,
connected porosity, relative permeability, capillary pressure, m
and n Archies constants, elastic moduli, and/or electrical
properties, or any combinations thereof.
In a method of the present invention, referred to as "the Point
Method," the process comprises the use of a logging tool, such as
Schlumberger's Modular Formation Dynamics Testing Tool (MDT)
capable of downhole images, gauge hole information, temperature,
pressure, fluid composition, pressure transient test, triple combo
test, T.sub.1 and T.sub.2 relaxation times from NMR, and/or other
available data. A traditional logging tool can be modified to
include a micro-sidewall coring device. Traditional sidewall cores
can range in size from several cm down to a few mm in size. Small
core samples, for example, of about 2 mm diameter by about 2 mm in
length or smaller can be produced from the micro-sidewall sampling
tool used in the present methods. The size of the core sample can
be selected to be suitable for subsequent CT scanning and digital
rock physics analysis. There can be an unexpected benefit from the
small core sample size. In brittle formations such as quartz and
carbonate dominated shale or tight-gas sandstone, coring tools and
percussive tools can fracture the rock making a large traditional
sample difficult or impossible to acquire. The very small size
required for digital rock physics makes it possible to produce
usable samples even in brittle formations. In addition, tight
formations such as shale are difficult to drill and may be
impervious to percussion sampling. Smaller sample sizes specified
in the present invention make sampling possible in formations that
otherwise would be difficult or impossible to sample. The core
sample cutting device could be percussion, rotary core, or other
techniques capable of producing a core sample. The depth of the
core sample is sufficiently deep to go beyond any expected heating
from the BHA of the drill strings, typically about one half inch or
more for most cases. The logging tool data can be combined and
analyzed to select an exact depth and azimuth for the micro-sample
retrieval tool. The depth selection can be made to avoid irregular
features in the formation such as fossils, burrows, shale stringers
in carbonates or sandstones, micro-fractures, and/or any other
non-representative features. The micro-samples are taken over the
same well interval or intervals as the logging tool. In this
interval, the micro-samples can be taken before, during, and/or
after the logging tool has finished its analysis and the
micro-sidewall coring device cuts a sample. The micro-sample thus
retrieved, combined with the downhole images, gauge hole
information, temperature, pressure, fluid composition, pressure
transient test, triple combo test, T.sub.1 and T.sub.2 relaxation
times from NMR, and/or other available data, can then be analyzed
using digital rock physics techniques to calculate k.sub.absolute,
total porosity, connected porosity, K.sub.r, P.sub.c, m and n
Archie's constant properties, elastic moduli, and/or electric
properties, and/or other properties for a given lithofacies. This
saves time and cost compared to producing a full core, doing a full
core CT scan and segmentation, selecting the representative plugs
for analysis, selecting micro-samples to verify representative
analysis, and then doing complete digital rock physics
calculations. The combination of logging tool data and digital rock
physics calculations done in the manner of the present invention
have the following advantages.
1. The digital rock physics calculations are more representative
because the location of the rock sample selected for analysis is
accurately matched with the location of the downhole fluid
analysis.
2. Calculation of relative permeability is improved when compared
to the estimates traditionally made by logging tools because the
calculations are direct (e.g., based on the scan of an actual rock
sample) and not indirect (e.g., such as estimated from pressure
transient and other test data).
3. Errors in porosity, permeability, relative permeability and
other rock properties estimated from logging tools increase as
porosity decreases, tortuosity increases and pore structure
complexity increases. This is because the correlation of downhole
tests such as pressure transient test, NMR, and other tests with
actual rock properties decreases as the rock structure becomes more
complex and less ideal. The combined logging tool and digital rock
physics methods of the present invention overcome these
deficiencies for directly computing rock properties in complex
formations such as shales, carbonates, and tight gas sandstone.
In another method of the present invention, referred to herein as
"the Trend Method," the process comprises the use of a logging
tool, such as Schlumberger's Modular Formation Dynamics Testing
Tool (MDT), capable of downhole images, gauge hole information,
temperature, pressure, fluid composition, pressure transient test,
triple combo test, T.sub.1 and T.sub.2 relaxation times from NMR
and/or other available data in combination with the digital rock
physics analysis as described in the Point Method above. Several
digital samples of the segmented rock sample can be retrieved by
the logging tool and numerically divided into a number of
sub-cubes. Eight sub-cubes (2.times.2.times.2) or more (e.g.,
3.times.3.times.3) may be used for example. Plotting pairs of
selected rock properties, elastic moduli versus porosity for
example, produces a trend. Such computationally-derived trends
comply with those produced in the physical laboratory on similar
rock material and/or with the trends predicted by relevant
theoretical models. Elastic wave properties in rock versus porosity
is one theoretical model which can be used for this purpose. The
relationship between elastic wave velocity and porosity can be
theoretically predicted in the absence of cracks in the rock to
produce a trend. When the calculated absolute permeability for a
sub-sample falls on the same trend line as predicted from the
elastic wave velocity versus porosity trend, the calculated
absolute permeability can be used as a data point to form a
permeability versus porosity trend because the selected sub-sample
does not contain cracks which would affect the absolute
permeability calculation. Such a sub-sample is representative of
the rock at in situ conditions. As referenced above, Rothman and
Zaleski have also studied the variation of properties such as
porosity and absolute permeability within a rock sample but their
interpretation is limited to property variation within the rock
sample. An unexpected benefit of the trends of the present
invention is that they can be used in the field to estimate
formation properties. This is unexpected because common wisdom
would suggest that in order to assess that variation in rock
properties one must take more rock samples from various locations
within the well or formation. However, with the present invention,
the variation within a lithofacies can be estimated from
sub-sampling and extrapolating the trends produced from the
sub-samples consistent with the expected or theoretical trend. In
this way the number of required samples to estimate variation of
rock properties within a given lithofacies is limited to the number
of lithofacies present in the formation. This results in
significant reduction in time and cost required to effectively
estimate rock and formation properties. The inventors have found
that the concept of trend is applicable not only for absolute
permeability but also for the elastic and electrical properties of
rock. The present invention also comprises the concept of verifying
these trends by theoretical rock physics and upscaling these trends
to the core and reservoir scale. Because the rock sample and fluid
analysis used in these trend calculations were selected from
approximately the same location within the formation, the resulting
trend curve can be used to validate and/or correct subsequent
estimates made by the logging tool. Moreover, the trend curves
generated can be used over a wide range of fluid properties and
fluid saturations to provide rapid and accurate estimates of well
properties and producibility.
The present invention also comprises the Trend Method as described
above where the parameters in the trend are permeability versus
porosity accounting for the natural porosity variations in the
formation.
The present invention also comprises the Trend Method as described
above where the parameters in the trend are elastic properties
versus porosity and mineralogy with in-situ fluids present in the
pores and covering a wide range of fluid saturations.
The present invention also comprises the Trend Method as described
above where the parameters in the trend are electrical formation
factor and the m and n Archie's constants properties versus
porosity and mineralogy with in-situ fluids present in the pores
and covering a wide range of fluid saturations.
The present invention also comprises the Trend Method as described
above where the parameters in the trend are relative permeability
versus water saturation, wettability, and/or capillary pressure.
Ranges for water saturation, wettability, and/or capillary pressure
can be selected to cover plausible ranges encountered during the
lifetime conditions in the reservoir.
The present invention also comprises building a database of results
from the Trend Method as described above for a wide range of
formation types including, but not limited to, sedimentary rocks
such as siliciclastic rocks (sandstones and shales) and carbonates,
igneous rocks, and metamorphic rocks. In addition, Trend Method
data is gathered for a range of synthetically manufactured porous
structures comprising sintered glass beads. The glass bead
formations are highly regular and are completely open pore
structures and as such can be considered "ideal" formations.
Logging tool tests that indirectly measure permeability and
relative permeability would correlate very well with an "ideal"
structure such as sintered glass beads. In the case of an "ideal"
formation an estimate of a rock parameter, such as permeability
from a logging tool, would fall very close to the same rock
property relationship produced by the Trend Method. Real world
formations can be highly "non-ideal". Logging tool estimates of
permeability, relative permeability, and other rock properties for
such "non-ideal" formations will deviate from the property
relationships produced by the Trend Method. A quadrant analysis of
calculations produced by digital rock physics and a logging tool is
shown in FIG. 4. This figure shows a typical trend generated by
sub-sampling a given rock sample and performing the Trend Method
analysis. The centrally-located dot in FIG. 4, which is the lighter
(grey) dot located where the quadrant lines intersect as
cross-hairs, is the absolute permeability and porosity obtained
from digital rock physics for the micro-sample extracted from the
formation using the logging tool. The dotted lines in FIG. 4
represent +/- three sigma variation for the digital rock physics
analysis performed on the same rock sample. Using the digital rock
physics data point (i.e., the centrally-located lighter (grey) dot)
as the center of the plots, the graph space is divided into four
quadrants labeled 1, 2, 3 and 4. The darker (black) dot located in
quadrant 1 in FIG. 4 represents the absolute permeability and
porosity estimated from the logging tool. In this case the dot
shown in Quadrant 1 is outside of the dotted lines representing
+/-3 sigma variation. The expected trend in absolute permeability
and porosity is from Quadrant 3 to Quadrant 2. The logging tool
estimate in FIG. 4 is not in Quadrant 3 or Quadrant 2 and it is
outside of statistically expected variation. Therefore, the logging
tool estimates may be considered questionable as the result of an
error such as trapped drilling mud in the rock pores or leakage in
the seal of the logging tool and the well bore. A similar
conclusion can be drawn if the logging tool estimate is in Quadrant
4 and falls outside of the +/-3 sigma variation. If the logging
tool estimate falls in Quadrant 3 or Quadrant 2 and is outside the
+/-3 sigma statistical variation, then the logging tool estimate
may indicate a different lithofacies or other variation in the
formation not present in the micro-sample extracted using the
logging tool.
The present invention also comprises a method to select the digital
rock analogue that is most representative of a facies, well, or
formation. FIG. 25 shows a graph of absolute permeability (k.sub.A)
versus porosity (.phi.). The graph includes points shown as the
shaded circles, other than black circle 311, which are produced
from digital rock physics using the Trend Method (310). Each of
these points is produced from a sub-sample of the digital rock
analogue using digital rock physics techniques. These points cover
a wide range of porosity and absolute permeability. Digital rock
physics is usually based on small sample sizes. As indicated,
sub-sampling even the small samples can yield useful information
about the range of rock properties in the formation or well, and
the sub-sampling produces a range of data points, such as for
absolute permeability versus porosity. Based on digital rock
physics alone, it can be difficult to know which of these points is
representative of a larger volume such as a lithofacies. Digital
rock physics alone cannot identify which of the sub-samples is most
representative of a facies, well or formation, at least not with
levels of accuracy that may be specified or needed. In FIG. 25, the
point shown as a black circle (311) is the measure of absolute
permeability and porosity obtained from the logging tool (e.g., an
MDT tool). The logging tool estimates absolute permeability and
porosity from a larger volume in the well bore than the sample
obtained for digital rock physics. The flow of fluids into the
logging tool represents vertical permeability and horizontal
permeability. The MDT tool, for example, makes an estimate of
absolute permeability by doing a test based on the area of a couple
of square centimeters, which is larger than any subsample. In
addition, the MDT pressure test draws fluid from an even larger
volume around to a location where the tool touches the well bore.
The fluids may be drawn into the logging tool from a distance of
several millimeters, several centimeters, several decimeters or a
meter or more. The point (311) therefore is a measure of the
porosity and absolute permeability obtained from a larger and more
representative volume of the rock in a facies, well, or formation.
The absolute permeability/porosity data (large volume) obtained
with the logging tool can be compared with the digital values
calculated for the sub-samples (small volume), and the sub-sample
which is closest to the MDT values can be selected. In this way,
the sub-sample which is most representative of a larger volume can
be selected. For example, the digital rock physics point (312)
closest to the measure from the logging tool was generated from a
sub-sample that has rock properties similar to the logging tool
point (311). This selected digital rock physics point (312) can be
used in subsequent digital rock physics calculations such as
relative permeability, capillary pressure, elastic modulus,
formation factor, and other properties of interest. This approach
to selecting a digital rock analogue can resolve a conflict in that
a small sample size is necessary for CT scanning but a small sample
size may not be representative of a large volume of the well bore.
Further, a benefit of the present method is that the indicated
selected sub-sample can be used for subsequent calculations with a
higher degree of confidence that the calculations are
representative of a significant portion of the well.
The present invention also comprises databases of deviations of
logging tool rock property estimates for various formation types
compared with the Trend Method curves produced with digital rock
physics. These databases are constructed such that subsequent
logging tool estimates of rock properties can be corrected in real
time or near real time during the logging process.
The present invention further comprises simulation of a logging
tool pressure transient test using the segmented digital rock
physics model produced on the sample of rock retrieved by the
logging tool as described above. The actual transient pressure
response is compared to the calculated response and a confidence
factor is assigned to the logging tool transient pressure test. In
this way, erroneous data, such as when the logging tool does not
form a secure seal with the well bore, is detected and erroneous
data can be discarded.
Referring to FIG. 5, a system 1 for in situ well logging and
retrieving core samples from a formation for integrated 3D image
analysis is shown. A well borehole 10 is shown penetrating earth
formation 12, which has an upper surface 13. The well borehole 10
is drilled before formation evaluation tools are lowered into the
borehole. Typically, the borehole 10 contains a combination of
fluids such as water, mud filtrate, formation fluids, etc., which
is not shown to simplify the illustration. For sake of simplifying
the illustration, the rig 20 is shown as assembled directly on a
dry land surface 13. A tool string 5 can be conveyed into and out
of the borehole 10 with a wireline 16. An MDT tool 14 and core
sample collection tool 15 are combined on the same tool string 5 in
a vertically stacked formation in this illustration. Tools 14 and
15 are shown connected with a field joint 7. One of tool 14 or tool
15 is shown in solid lines and the other in broken lines in FIGS. 5
and 6 to emphasize which tool is located in a well interval "x" or
well/formation depth of interest at that time. It will be
understood that rig 20 alternatively can be mounted on an offshore
drilling platform in a body of water (e.g., ocean, sea), wherein
surface 13 would be the seabed or ocean floor, and piping (not
shown) could extend from the rig 20 through an intervening body of
water to the borehole 10 in the sea or ocean bed (13) through which
tools 14 and 15 could be conveyed on before reaching and entering
into the borehole 10. An MDT tool 14 or other in situ formation
test tool useful for well logging is shown being lowered into the
well borehole 10 on tool string 5 attached to an armored,
multiconductor cable or "wireline" 16. The tool 14 can be used to
analyze the formation 12 at least through well interval "x" of the
well borehole 10. The well interval "x" may be selected, for
example, to be at a depth proximate to a known or possible
reservoir of interest for in situ logging and retrieval of core
samples for 3-D imaging as part of some of the present methods. The
vertical distance of well interval "x" can vary depending on the
particular site. The well interval "x" may be, for example, from
about 10 feet to about 2,000 feet, or from about 25 feet to about
1,000 feet, or from about 50 feet to about 500 feet, or other
distances. The tool 14 can be cylindrically-shaped in
cross-section, or have another cross-sectional geometry sized to
fit within the space bounded by the borehole wall 11 defining the
borehole 10 and forming part of formation 12, for substantially
unobstructed vertical movement of tool 14 up and down the borehole
at the well interval or intervals of interest for in situ
measurements and sampling and the borehole space above that
location(s). Location 15A above the external surface 13 surrounding
the borehole 10 can be where core samples or other formation
samples, such as obtained with a different tool 15 shown in broken
lines in FIG. 5, are retrieved for ex situ 3D image analysis, lab
analysis, and/or other analysis relative to the borehole 10. In
this non-limiting illustration, tools 14 and 15 are run on a single
wireline 16. Although advancement of the tool 14 and tool 15 in the
borehole 10 is shown using only a wireline 16 in a substantially
vertically-oriented borehole 10 in the illustrations of FIGS. 5 and
6, other tool string conveyance systems may be used. A conventional
or otherwise suitable tough logging conditions system (TLC),
pipe-conveyed descent system, coiled tubing system, or downhole
tractor system (not shown) known in the industry may be adapted for
use in conveying tools 14 and 15 to the well interval of interest
for downhole logging and core sample retrieval operations. For
example, a specialized delivery system may be helpful or needed
such as where the well interval of interest is in a segment of the
borehole which is non-vertical.
Formation tester tool 14 can be, for example, an MDT tool capable
of downhole characterization of formation fluids. The MDT tool can
be capable of such downhole characterization of formation fluids
without the need for transfer of fluid samples from below the
surface to a laboratory for surface analysis. Tool 14 can include,
for example, a power module(s), power conditioning circuitry, tool
control processors, sensors required for the measurement(s) to be
made in one or more modules, fluid processing modules, and
telemetry circuitry to transmit the information back up the
wireline 16, or other components. The wireline 16 can be played out
from a winch 22, such as manually controlled by an operator within
a well logging truck or skid (not shown) or automatically. The
wireline 16 can be lowered into the borehole 10 after passing over
a sheave wheel 18, which is in turn supported by rig 20, and after
passing under a lower sheave wheel 18A. As indicated, tool string 5
is attached to wireline 16. The wireline 16 can include conductors
that provide for power, control signals to and control and data
information from the MDT or other in situ formation test tool 14.
The conductors can be connected to an electrical control system 24,
which can generally include a control processor 24A operatively
connected with the tool string 5. Logging tool and sample
collection operations forming parts of methods of the present
invention can be embodied in a computer program that runs in the
processor 24A. In operation, the program can be coupled to receive
data, for example, from the downhole fluid analysis module(s), via
the wireline 16, and to transmit control signals to operative
elements of the borehole tool string 5. The computer program may be
stored on a computer usable storage medium 24B (e.g. a hard disk)
associated with the processor 24A, or may be stored on an external
computer usable storage medium 26 or other recorder and
electronically coupled to processor 24A for use as needed. The
storage medium 26 may be any one or more of presently known storage
media, such as a magnetic disk fitting into a disk drive, or an
optically readable CD-ROM, or a readable device of any other kind,
including a remote storage device coupled over a switched
telecommunication link, or future storage media suitable for the
purposes and objectives described herein. For example, the logging
data stored at the storage medium 24B or external storage medium 26
can be transferred to one or more computers 27 having program
instructions for carrying out further analysis of the logging data,
3D image analysis, and/or subsequent integrated formation property
modeling as described herein. The computer or computing system 27
may include one or more system computers, which may be implemented
as a personal computer or server. However, those skilled in the art
will appreciate that implementations of various techniques
described herein may be practiced in other computer system
configurations, including hypertext transfer protocol (HTTP)
servers, hand-held devices, multiprocessor systems,
microprocessor-based or programmable consumer electronics, network
PCs, minicomputers, mainframe computers, and the like. The control
system 24, the external storage medium 26, and computer 27 can be
connected to each other for communications (e.g., data transfer,
etc.), via any of hardwire, radio frequency communications,
telecommunications, internet connection, or other communication
means. Further, the data and other logging related information
collected at the control system 24 and/or storage medium 26 may be
visually displayed on a monitor, CRT, log chart, or other visual
means of display (not shown) at the site and/or offsite. The tool
data and any initial interpretation information thereon can be
communicated, for example, via satellite or land lines (not shown)
to an offsite or remote location for further analysis relevant to
logging information or formation characterization, including other
interpretation software in combination with 3D image data obtained
from samples collected in the same well interval of the well
bore.
As shown in FIG. 6, a core sample retrieval tool 15 can be lowered
with tool string 5 on wireline 16 into the well borehole 10 for
retrieval of core samples 17 from the formation 12, at least in the
well interval "x" where in situ well conditions are being logged by
tool 14 in the same or different run or pass. Tool 15 differs from
any fluid sample extraction functionality of MDT tool 14 in that
tool 15 is adapted to extract core plugs or other solid containing
forms of samples from the formation, such as from the sidewall
thereof. Retrieved core samples 17 can be collected from the tool
15 for 3D image analysis after it is lifted out of the well
borehole 10 and above external surface 13, such as indicated by
position 15A of tool 15 in FIG. 6. As shown in FIG. 6, core samples
17 (or other types of formation samples) removed from the formation
12 using core sample retrieval tool 15 can be transported to a
computer tomographic ("CT") or SEM scanner 19. Tool 15 can include,
for example, a power module(s), power conditioning circuitry, tool
control processors, a remotely controllable rotary core or
percussion sampling module, and telemetry circuitry to communicate
via cable 16, or other components. The CT scanner or SEM scanner
can use x-rays for analysis of internal structure of the samples,
for generation of three dimensional (3D) images 21 of the core
samples or other forms of samples retrieved from the formation. The
images so generated can be in numerical form and their content will
be further explained below. After scanning, the samples can be
saved for further analysis or may be discarded. In general, the
instrument used to scan the core samples 17, or other types of
retrieved samples from the formation (e.g., percussion samples,
cuttings, etc.), can be selected based on how small are the pores
in the rock and how much resolution in needed to produce a usable
image. Examples of suitable CT scanners for making images usable
with methods according to the present invention, include, for
example, 3D tomographic x-ray transmission microscopes, such as
MicroXCT-200 and Ultra XRM-L200 CT, which are made by Xradia, Inc.
(Concord, Calif. USA). For coarser samples, such as carbonates or
sandstones, the MicroXCT-200 may provide sufficient resolution.
When smaller pore samples, such as some shales, are tested, the
higher Ultra XRM L200 CT may be useful. In addition, very dense
rock formations, such as some shales, can require resolution beyond
X-ray CT scanners. In these situations, scanning electron
microscopes can be used instead. An example of an SEM than can be
used is Zeiss Auriga SEM. In the present example, the 3D image
output (images) 21 generated by the CT scanner 19 can be
transferred to a computer 27 having program instructions for
carrying out the indicated logging data analysis, the image
analysis, and subsequent formation property modeling to provide
formation modeling output/results 29, as described below.
Multiple tools can be lowered on wireline 16 down the borehole 10
for logging and sample retrieval, and possibly other operations
(e.g., CMR analysis), in combination on the tool string 5 in a
single run, or in separate runs. In FIGS. 5 and 6, the MDT tool 14
and core sample retrieval tool 15 can be combined, for example, in
one run down the borehole 10, even though the tools are operated
sequentially. The tool 14 and 15 can be provided as separate
modules which are operatively connected together with a field joint
or connector 7. The field joint or connector may provide an
electrical connection, a hydraulic connection, a flowline
connection, or combinations of these, depending on the needs of the
tools on the wireline. Field joints are shown, for example, in U.S.
Patent Application Publication Nos. 2006/0283606 A1 and
2009/0025926 A1, and U.S. Pat. No. 7,191,831, which are
incorporated herein by reference in their entireties. As indicated,
the formation test tool 14 and the core sample retrieval tool 15
can be combined on a single tool string 5 lowered on a single
wireline 16 into the well interval "x" of interest. As shown in
FIGS. 5 and 6, tools 14 and 15 can be sequentially advanced into
the well interval "x" of interest for in situ downhole property
measurements or core sample collection, respectively.
Alternatively, tool 15 can be located below tool 14 on tool string
5. Although not illustrated in FIGS. 5 and 6, the logging tool 14
may comprise an MDT tool which integrally incorporates at least one
core sample retrieval module within a common housing. These various
options for deploying tools 14 and 15 share the method of using
both at least an MDT tool for in situ logging information
collection in a selected well interval, and a sample retrieval tool
(separate from or integrated with the logging tool) for extraction
of samples over the same well interval from the wellbore for 3D
image scanning and analysis outside the well borehole and
formation. Other formation analysis tools, such as a combinable
magnetic resonance (CMR) tool (not shown), may be combined in the
tool string in the same or separate runs with respect to the MDT
tool and core sample retrieval tool. The number of tools that may
be included in any single run versus using multiple runs over the
same well interval may be influenced by a number of factors,
including compatibility, power consumption, and telemetry
requirements, and other factors.
FIG. 7 is an enlarged view of an MDT tool 14 configured in a
modular format. As noted above, the MDT or other in situ formation
test tool 14 can be lowered on armored cable 16 to the interval or
zone of interest "x" where in situ formation analysis (and core
sample retrieval for ex situ 3D imaging analysis), is desired. In
the instance of a formation test tool, the tool 14 can be
stationary when it makes its measurements, or moving, depending on
the type of measurement. The MDT formation test tool 14 in FIG. 7
is shown as being comprised of several modules 301-308. The modules
301-308 of tool 14 can be joined to each other using field joints
(not shown). The sensor and testing devices of tool 14
alternatively can be assembled within a common housing. Modular
tool tester designs can permit the user to customize and configure
the tool 14, including on-site, to meet requirements depending on
the needs of a particular well evaluation. Accordingly, a lesser or
greater number of modules can be combined into tool 14. As
indicated herein, a number of in situ formation and well borehole
conditions can be used in various present methods. Accordingly, the
MDT tool can be customized in modular form to provide evaluations
for those and other in situ parameters of interest. In this regard,
Table I shows several exemplary logging tool configurations 1-5 of
modules of tool 14 with respect to FIG. 7, which, as indicated, can
include all modules 301-308 or lesser combinations thereof. Some
module parts of configuration 5, for example, are illustrated
schematically in FIG. 7, while other different types of modules of
other configurations are not shown to simplify the
illustrations.
TABLE-US-00001 TABLE I Logging Logging Logging Logging Logging Tool
Tool Tool Tool Tool Config. 1 Config. 2 Config. 3 Config. 4 Config.
5 Module (module (module (module (module (module Number type) type)
type) type) type) 301 Power Power Power Power Power 302 Hydraulic
Hydraulic Pump-out Multisample Pump-out (fluid) 303 Single- Single-
Hydraulic Sample Sample probe probe power chambers chambers (fluid)
(fluid) 304 Single- Single- Single- Pump-out Multisample probe
probe probe (fluid) 305 Sample Dual- Dual- In situ In situ chambers
probe packer live fluid fluid analysis (fluid) analyzer module 306
Sample Flow- Flow- Hydraulic Hydraulic chambers control control
power Power (fluid) 307 None Sample Sample Single- Single- chambers
Chambers probe probe (fluid) (fluid) 308 None None None None
Dual-packer
Other combinations of these and other MDT modules in formation
tester tools can be used, provided the resulting tool configuration
can be sufficient to collect the requisite in-situ data from the
well borehole and formation for the present methods. A commercial
supplier of such MDT formation tester tools (Schlumberger), has
characterized a modular tool configuration similar to Configuration
1 as a basic MDT for pressure, permeability, and fluid sampling,
Configuration 2 as a multi-probe vertical interference testing,
Configuration 3 as vertical interference testing with a
probe-packer, and Configuration 4 as low shock PVT-quality
sampling. Tools similar to Configuration 5 are described in greater
detail, for example, in U.S. Patent Application Publication No.
2009/0078036 A1, which is incorporated herein by reference in its
entirety. As indicated, other combinations of these or other known
MDT modules may be used. Other commercially available devices which
may be adapted for use as tool 14, include, for example, a
Reservoir Characterization Instrument (RCI) of Baker Atlas.
Illustrative details on the equipment and functions of the modules
indicated in Table I can be understood from various commercial MDT
tools. Downhole fluid analysis can be done using one or more fluid
analysis modules in an analysis module, for example, Schlumberger's
Modular Formation Dynamics Tester (MDT). The power module of the
MDT tool can be, for example, an electric power module which can
convert AC power from the surface to provide DC power for all
modules in the tool. The hydraulic power module, for example, can
contain an electric motor and hydraulic pump to provide hydraulic
power for setting and retracting the single- and dual-probe
modules. The single-probe module can contain, for example, a probe
assembly with packer and/or backup pistons, and can include, e.g.,
pressure gauges, fluid resistivity sensors, temperature sensors,
strain gauge, a pretest chamber, and so forth. The volume, rate and
drawdown of the single-probe module can be controlled from the
surface to adjust to the test situation. The dual-probe module can
contain, for example, two probes mounted back-to-back and
approximately 180.degree. apart on the same assembly body. When
combined with a single-probe module, the dual-probe module can form
a multi-probe system capable of determining horizontal and vertical
permeability. During a typical test with the dual-probe module,
formation fluid can be diverted through a sink probe to a pretest
chamber (not shown) in a flow control module. The dual-probe
module, in conjunction with the pressure measured at the vertical
probe from the single-probe module, can measure the pressure at
both probes, and these measurements can be used, for example, to
determine near-wellbore permeability anisotropy. The dual-packer
module can use multiple inflatable packers, set against the
borehole wall, to isolate and seal a section of the formation
(e.g., about 2 to about 15 feet, or about 3 to about 12 feet, or
other section lengths), and provide access to the formation over a
wall area to allow fluids to be withdrawn without dropping below
the bubble point, and it can provide a permeability estimate. The
dual-packer module can be used to make pressure measurements and
take fluid samples, and can be used for in-situ stress testing and
mini-frac testing. The pump-out module can be used to pump unwanted
fluid (e.g., mud filtrate) from the formation to the borehole, so
representative fluid samples can be taken, and it also can be used
to pump fluid from the borehole into the flowline for inflating the
packers of the dual-packer module, and also can pump within the
tool, for example, from a sample chamber to the inflatable packers.
Further, as known in the field, a formation field sample
essentially free of contamination from the drilling mud filtrate
may be provided by using dual pumps to withdraw both the reservoir
fluid and the surrounding mud filtrate simultaneously into separate
flowlines, to divert the mud filtrate into the borehole while a
relatively purer stream of formation fluid can be obtained for
measurement and collection in real time. The live fluid analyzer,
which may be used in combination with the pump-out and dual
inflatable packers, can be used to provide downhole fluid analysis
in real time. The live fluid analyzer can measure optical
properties of the fluid in the flowline, and can employ an
absorption spectrometer that utilizes visible and near infrared
light to quantify the amount of reservoir and drilling fluids in
the flowline. Another sensor in the live fluid analyzer can be a
gas refractometer, which can be used to differentiate between gas
and liquid in known manners. The fluid analysis module can be, for
example, a live fluid analyzer, an optical analyzer, or ultra fluid
analyzer, such described, for example, in U.S. Patent Application
Publication No. 2009/0078036 A1, which is incorporated herein by
reference in its entirety. An ultra fluid analyzer, for example,
can be used for flowing and captured analyses of downhole fluid
samples to provide compositional and physical property
measurements, such as density, viscosity, and the like. The flow
control module can be a pretest chamber where the flow rate can be
accurately measured and controlled, and it can be used during
sampling that requires a controlled flow rate. The flow control
module can create a pressure pulse in the formation large enough
for multiprobe measurements. The sample and multisample chambers
can be designed to retrieve two or more formation fluid samples
during a single run into the well for laboratory analysis outside
the well. As indicated, use of MDT tool modules having operability
for downhole characterization of formation fluids, such as the live
fluid analyzer, optical fluid analyzer, or ultra fluid analyzer.
Additional sensors for fluid and formation characterization, e.g.,
NMR imaging sensors, acoustic sensors, or other sensors may be
located in any of the modules of formation test tool 14. Other
details on these and related modules are described, for example, in
U.S. Patent Application Publication No. 2009/0079036 A1 and U.S.
Pat. No. 6,856,132, which are incorporated herein by reference in
their entireties.
In using MDT tool 14, the tool can be lowered to a desired test
depth within the borehole 10. Modules having pistons, back-up
shoes, and/or inflatable packers or similar devices can be actuated
to move the tool 14 into a vertically-stationary position relative
to the borehole wall 11, to restrict or block some fluid movements
within the borehole, and so forth. It will be appreciated that the
test program can vary with the parameters to be measured, the
nature of the formation 12, and the number of samples taken for in
situ evaluation. After completion of the desired in situ
measurements, the tool 14 can be deactivated and the
pistons/inflatable packers returned to the retracted/deflated
positions/states, and the tool 14 may then be moved to a different
depth for additional testing or returned to the surface 13 after
completion of all tool operations. For example, after moving tool
14 out of the well interval "x", tool 15 can be moved into position
in the same well interval "x" for core sampling, or vice versa.
Referring again to FIG. 6, the core sampling done by tool 15 can
be, for example, sidewall coring or percussion. In sidewall coring,
the wireline coring tool can be run into the hole in the same run
when the well is logged with tool 14 or in a separate run. The
sidewall coring tool can be operable, for example, for sidewall
rotary coring or percussion type core sampling. In more detail in
FIG. 8, tool 15 is shown as a sidewall rotary coring tool having a
small extendable/retractable robotic core bit 41 or other drill bit
device having similar functionality, which can be used to bore a
core sideways into the formation. The drill bit 41 can be, for
example, a horizontal hollow rotary diamond drill bit. The drill
bit can be hollow tubular-shaped defining a receiver tube with
diamond cutting edges surrounding the distal opened end thereof.
The drill bit can be rotated, for example, at about 2,000 rpm or
higher. A backup shoe 43 or similar device can be extended and used
opposite the rotary drill bit of the tool to hold or brace the tool
15 securely against an opposite wall 11 of the formation 12.
Alternatively, an inflatable packer module can be used to hold the
core sampling tool 15 in place during sidewall coring. The core
sampling tool 15 also can include additional modules or components,
which can be commonly used in sidewall coring tools of this type,
such as, for example, an electronics control and power module 44, a
compensator module 46, and hydraulic module 48, which can be
configured in conventional or commercially known manners. In a
sample collection mode of operation of tool 15, a cut core sample
45 is captured in the hollow interior of the drill bit 41 from the
sidewall drilling, which core sample can be broken loose from the
formation, such as, e.g., by slight vertical movement of the drill
bit 41, and withdrawn into the coring tool 15 for retrieval from
the borehole. For example, the core sample can be punched into a
receiver compartment 47 (e.g., tube) of the tool 15. Then, the tool
15 may be moved to another spot in the well interval or intervals
of interest within the borehole, and the bit can be extended and
used again to retrieve another sidewall core sample, and so on,
until the desired number of cores are retrieved from the desired
locations. Alternatively, a single core sample can be retained in
the hollow bit for retrieval after the tool is pulled out of the
borehole. Each sample can be isolated for identification, for
example, including recording of data on the formation location
(e.g., borehole depth, core depth), and time. The core can be
recovered as a small cylindrical-shaped plug of the formation. The
core samples obtained with the rotary sidewall coring tool can be,
for example, about one inch (25 mm) or less in diameter by about
1-2 inches (25-51 mm) or less in length, or smaller, such as about
2 mm or less in diameter by about 2 mm or less in length.
As indicated, rotary cores are only one type of rock formation
sample that may be analyzed according to methods of the present
invention. Percussion sidewall core samples may be obtained using
tool 15 on tool string 5 for withdrawal from the well borehole 10
as part of the wireline well evaluation techniques. The percussion
method, if used for sample collection, can use, for example, a high
explosive charge to propel a short core barrel into the formation
at high speed to embed the core barrel into the formation rock, and
then the core barrel is withdrawn by a strong wire. Typically,
cores of about 1 inch (25 mm) or less in diameter and about 1-2
inches (25-51 mm) or less in length can be retrieved by this
method. The core samples obtained using tool 15 and withdrawn from
the borehole can then be 3-D imaged and analyzed in conjunction
with well logging information and evaluations thereon in accordance
with present methods.
Referring to FIGS. 9 and 10, examples of the indicated point method
and trend method are shown in more detail with reference to steps
101-117 and 201-219, respectively.
Referring to FIG. 9, steps 101-117 of the point method can relate
to methods and operations such as indicated in the following
discussions.
Step 101
The lowering of a tool string carrying the formation tester tool on
a wireline into a well borehole and positioning of a formation
tester tool downhole in the well interval of interest for
evaluation can be implemented in manners such as described with
respect to FIGS. 5-8.
Step 102
Sensors and analyzers provided on tool 14 are used to take
measurements in the well interval of interval of downhole fluid
conditions, which can be downhole measurements which are
processible to determine measures of downhole fluid properties
(e.g., fluid composition, pressure, viscosity, etc.) which, in
turn, can be used in estimating permeability in subsequent steps of
the present method. The tool used to take these downhole
measurements can be, for example, a logging tool similar to one or
more of logging tool configurations 1-5 and the referenced
patent/application publications.
Step 103
A pressure transient test can be performed in a known manner, for
example, using a formation tester (e.g., Schlumberger Modular
Dynamics Tester (MDT)), to measure the transient build up in the
pore pressure following an extraction of a fixed volume of
formation fluid. Under suitable assumptions of flow regime near the
probe, the effective permeability (k.sub.e) of the formation can be
related to the pressure build up.
Step 104
A CMR test can be performed in a conventional manner, such as by
taking NMR measurements with a formation tester having that
capability, such as can be provided by the Schlumberger Combinable
Magnetic Resonance tool known by the acronym CMR (or equivalents
thereof). An example of methods of performing a CMR test which may
be adapted to the present methods includes those shown in U.S.
Patent Application Publication Nos. 2008/136410 A1 and 2011/0054796
A1, which are incorporated herein by reference in their
entireties.
Step 105
Downhole measurements taken in step 102 using tool 14 are
processed, such as at processor 24A and/or at computer 27 shown in
FIG. 1, to provide measurements of fluid composition, temperature,
pressure, viscosity, and/or other fluid properties.
As indicated in the discussion of step 102, various downhole fluid
properties in the well interval of interest can be determined.
Step 106
Porosity can be estimated from the CMR test results of step 105 in
any conventional manner applied for that purpose.
Step 107
Permeability can be estimated using formation data collected in
steps 103, 104, 105, and 106. Known algorithms can be used (e.g.,
Darcy's Law). An example of a method of estimating permeability,
which may be adapted to the present methods, includes that shown in
U.S. Patent Application Publication No. 2011/0054796 A1, which is
incorporated herein by reference in its entirety.
Step 108
As discussed in regards to FIG. 4, a micro-sidewall core sample can
be obtained using sidewall rotary coring tool, such as disclosed
herein, or other suitable coring methods.
Step 109
A sample can be prepared for scanning, the sample can be directly
taken from a standard coring tool or it may be a "micro" sample
(e.g., 2 mm diameter or less by 2 mm or less in length). Such micro
samples typically are not taken in the industry currently because
physical laboratories cannot test such a small sample. Digital
methods, however, can handle a smaller sample. Taking a smaller
sample can be an advantage, for example, when the rock formation is
very tight and difficult to cut.
Step 110
As discussed in regards to FIG. 2, a CT or SEM scan of the core
sample retrieved from the well interval of interest can be
performed. As indicated in the discussion of the CT/SEM scan in
FIG. 2, one difference between the Xradia MicroXCT-200 and Ultra
XRM-L200 CT is resolution. For coarser samples such as carbonates
or sandstones, the scanner model MicroXCT 200 may provide
sufficient resolution. When smaller pore samples, such as some
shales are tested, the higher Ultra XRM L200 CT scanner may be
useful. In addition, very dense rock formations, such as some
shales, can require resolution beyond X-ray CT scanners. In this
case, scanning electron microscopes can be used instead, such as a
Zeiss Auriga SEM. In general, the SEM instrument used is selected
based on how small the pores in the rock are and how much
resolution is needed to produce a usable image. As indicated, the
choice of scanner depends upon the size of the grains and pores in
the rock sample. It is common that one scanner is used, but more
than one scanner may be used if a low resolution scan is initially
used to select an appropriate area on the rock for a higher
resolution scan.
The voxel size of the images obtained with the CT or SEM scanner
can depend on the type of scanner used and the resolution. For
X-ray CT scanners (typically used for carbonates and sandstones)
the voxel size can range, for example, from about 500.mu. (microns)
to about 65.mu.. For scanning electron microscopes (SEM)(typically
used for shales), the voxel size can range, for example, from about
20 nm (nanometers) to about 5 nm. The scanners typically output a
series to two-dimensional arrays of values representing the gray
scale values from the scanner. For X-ray CT scans, approximately
1024 scans, for example, can be used to produce the "stack" of
"images". There is no technical reason why this number could not be
changed. The resolution is determined by the thickness of the
sample. The resolution is the thickness divided by 1024 in this
case. For SEM scans, the resolution can be set at 5 nm and the
number of scans can be adjusted depending on the thickness of the
sample. Other resolutions such as 7.5 nm or 10 nm can be selected,
for example, for SEM scans.
Step 111
Gray scale image creation of this step is produced from the arrays
generated by the CT scanner in the previous step 110. The gray
scale image creation of this step can be similar to the gray scale
process in U.S. Patent Application Publication No. 2010/0128932 A1,
which is incorporated herein by reference in its entirety.
The CT scan image output produced by a CT or SEM scanner, such as
3D image output 21 of scanner 19 in FIG. 2, can be a 3D numerical
object consisting of a plurality of 2D sections of the imaged
sample. Each 2D section includes a grid of values each
corresponding to a small region of space defined within the plane
of the grid. Each such small region of space is referred to as a
"pixel" and has assigned thereto a number representing the image
darkness (or for example the density of the material) determined by
the CT scan procedure. The value ascribed to each pixel of the 2D
sections is typically an integer that may vary between zero and 255
where 0 is, e.g., pure white, and 255 is pure black. Such integer
is typically referred to as a "gray scale" value. 0 to 255 is
associated with eight digital bits in a digital word representing
the gray scale value in each pixel. Other gray scale ranges may be
associated with longer or shorter digital words in other
implementations, and the range of 0 to 255 is not intended to limit
the scope of the invention. For the purpose of simulating a
physical process using such a numerical object (the gray scale),
however, the numerical object can be processed so that all of the
pixels allocated to the void space in the rock formation (pore
space) are represented by a common numerical value, e.g., by only
255s, and all of the pixels associated with the rock matrix (or
rock grains) are represented by a different numerical value, for
example, zeroes. Subsequently, the resulting numerical object can
be normalized so that the pore spaces are represented by, for
example, ones and the rock grains are represented by zeroes. The
foregoing may be described as converting the image into a binary
index. In other examples, the image may be converted into an index
having any selected number, n, of indices. It has been determined
that sufficiently accurate modeling of some rock petrophysical
parameters or properties, e.g. permeability, may be obtained using
a binary index, in which one value represents pore space and
another single value represents rock grains.
Step 112
The gray scale image created in step 111 can be cropped and
segmented. The cropping and segmentation step can be done with
Avizo.RTM. software adapted for use in the present methods.
Standard modules of Avizo.RTM. software can be used to process the
arrays to do calculations for cropping the matrix. Avizo.RTM.
Edition software, such as either the Avizo.RTM. Earth or Avizo.RTM.
Fire options of the Avizo.RTM. standard product can be used for
cropping the matrix. The screen menu options used for this purposes
can vary. After cropping the matrix, the image is segmented.
Program modules can be programmed into Avizo.RTM. software for
executing this operation. The Aviso-based segmentation package uses
various image-processing techniques that include (a) noise
reduction; (b) identifying the boundaries of the grains based on 3D
surface gradients of the gray scale encountered in the original
image; and (c) thresholding based on this image enhancement and
focus sharpening. Another segmentation process which can be adapted
for use in the present methods is in U.S. Patent Application
Publication No. 2010/0128932 A1, which is incorporated herein by
reference in its entirety.
Step 113
A segmented 3D matrix is produced from the cropped and segmented
matrix of step 112. Image segmentation provides a segmented 3D
image of the rock sample including image elements for rock grain
and for pore sizes. The 3D segmented image can be stored or
displayed in a computer and can be used as input to one or more
rock property characterization models. Another segmented 3D matrix
process which can be adapted for use in the present methods is in
U.S. Patent Application Publication No. 2010/0128932 A1, which is
incorporated herein by reference in its entirety.
Step 114
In this step, two different types of adjustments can be made to the
3D matrix to represent in-situ conditions with respect to (1)
introducing cracks in the 3D matrix cracks that exist in-situ which
are not imaged, and to (2) closing image cracks generated due to
unloading the core sample from the in-situ stress to benchtop
stress.
With respect to these adjustments in the 3D matrix, to compute the
in-situ values of porosity, permeability, and electrical
resistivity of rock represented by its CT-scan or FIB/SEM image,
elastic properties of the rock sample are first computed assuming
that the mineral phase is pure quartz. If the result matches a
theoretical in-situ-stress model where the elastic properties are
calculated assuming that the rock is also pure quartz, it can be
concluded that the image is relevant to the in-situ conditions and,
hence, all other properties computed on this image are also likely
to be relevant to the in-situ-stress conditions. If the elastic
properties thus computed do not match the theoretical-model
prediction, the image is processed to introduce or eliminate the
compliant cracks that appear in the physical sample due to its
unloading from the in-situ to benchtop stress. This can be
accomplished either by altering an image or taking its subsamples.
Once the elastic properties computed on the image thus processed
(or some of its subsamples) match the above-mentioned theoretical
criterion, it can be concluded that all other properties computed
on the same image are valid at in-situ conditions as well.
The physical properties of rock vary with varying differential
stress. The differential stress at in-situ conditions depends on
the depth of burial, tectonic forces, and the pore pressure. It may
be as high as 40 MPa (about 6,000 psi). At the same time, the
CT-scan and FIB/SEM images of rock samples used in computational
rock physics are taken at ambient (benchtop) conditions with
essentially zero differential stress. The challenge becomes how to
infer the in-situ rock properties from the images taken at benchtop
conditions.
In meeting this challenge, four types of physical properties are
considered: the total porosity; the elastic properties (the bulk
and shear moduli and elastic-wave velocity); the absolute
permeability; and the electrical resistivity. The variations of all
these properties with stress are directly related to the changes in
the pore geometry of rock. Usually, as a sample is brought from the
subsurface, it expands resulting in porosity increase. However, the
most important result of reducing the stress is that the pore space
variations are not geometrically congruent. As shown in FIGS.
11A-B, new pores, such as very thin cracks, may open up on
benchtop. Also, contacts between mineral grains that existed
in-situ may relax or simply disappear on benchtop. Such new
features appearing in the mineral framework generally weakly affect
the total porosity. However, because these newly opened cracks are
extremely compliant, they may strongly affect the elastic-wave
velocity. The permeability behavior is somewhat different. In cases
where robust flow paths exist in-situ, the addition of thin cracks
due to stress reduction cannot significantly affect permeability.
However, where permeability is small or non-existent in-situ and
flow paths are exclusively dependent on cracks, additionally
generated cracks may strongly increase permeability (in relative
terms) and even make a sample impermeable in-situ permeable on
benchtop. This usually occurs in tight gas sandstone and tight
shale. The stress dependence of the electrical resistivity of
porous rock is qualitatively similar to the permeability
behavior.
An example of laboratory measurements of the elastic-wave velocity
and permeability versus confining stress conducted on a tight gas
sandstone sample of about 0.05 porosity is shown in FIGS. 12A-C.
Simultaneous increase in the velocity and reduction in permeability
is observed as the stress increases from its benchtop value to high
value, close to the in-situ stress. It has been deduced from this
example that if the measured elastic-wave velocity is close to the
expected in-situ-stress value, the permeability measured at the
same conditions is also close to the in-situ value. Where velocity
and permeability data are obtained on the same sample and at the
same stress, but the magnitude of the stress is not registered,
then there can be a question on how to determine whether these data
are relevant to the in-situ (rather than benchtop) conditions. A
resolution has been developed from comprehensive rock physics
models that robustly relate the elastic-wave velocity to porosity
at in-situ stress conditions. Such models have been developed for
the elastic properties, but not permeability per se. Therefore, the
velocity can be used as an indicator of stress and propose that if
the measured velocity matches the in-situ-stress model, so will the
permeability (and other rock properties) if measured concurrently
with the velocity. An example of applying such model-based
diagnostics to the tight gas sandstone data displayed in FIGS.
12A-C is shown in FIG. 13A where only the upper two velocity data
points (at 50 and 100 MPa confining stress) match the
velocity-porosity model curve. From this, it can be concluded that
only two permeability values concurrent with these two velocity
values are relevant to the in-situ conditions (FIG. 13B).
Further, although the rock properties are computed on rock images
obtained at benchtop conditions, these images may or may not reveal
the very thin cracks that appear in the sample due to confining
stress reduction, and a determination is needed on whether the
image contains these cracks or not. The answer is similar as for
the physical measurement, and the procedure can be as follows:
(a) compare the computed elastic-wave velocity to the relevant
in-situ-stress rock physics model velocity-porosity curve,
(b) if the computed data match the model, the image reflects the
in-situ conditions and, hence, the porosity, permeability, and
electrical resistivity computed on this image correspond to in-situ
conditions,
(c) if the computed velocity data do not match the model, process
the image to either remove the cracks (if the computed velocity is
smaller than predicted by the model) or introduce the cracks (if
the computed velocity is larger than predicted by the model) and
re-compute the velocity until a match between the computed and
theoretical velocity is obtained (an alternative is to subsample
the sample and check whether one or more of the subsamples meet the
velocity criterion), and
(d) the image thus processed (or a subsample of the image) is
relevant to the in-situ conditions and, hence, the porosity,
permeability, and electrical conductivity computed on this image
are close to their in-situ values.
Finally, the computed velocity data has to be compared to a
theoretical curve calculated for the same mineralogical composition
as used in these computations. For the purpose of diagnosing
whether the rock image under examination is appropriate for
computing permeability and electrical resistivity at in-situ
conditions, the elastic property computations can be conducted for
any mineralogical composition (e.g., pure quartz). These results
are then compared to the pure-quartz theoretical curve.
For introducing cracks, a 3D rock image in FIG. 14 (top-left) is
shown for sake of illustration. In FIG. 14, the original image is
top left, and the other five images show gradual introduction of
thin cracks into this image (left to right and top to bottom) to
simulate varying state of confining stress. More cracks appear as
the stress is reduced. It may be questioned whether the
permeability computed on this image is relevant to the in-situ
(high confining stress) conditions of the rock. To solve this
question, it is assumed that this sample is pure quartz, and its
elastic properties are computed and compared to the pure-quartz
theoretical curve (FIGS. 15A-B). The directional permeability
values computed on the same digital sample are shown in FIGS.
16A-C. As indicated, the y-direction permeability is zero (FIG.
16B).
It is clear from FIGS. 15A-B that the computed P- and S-wave
velocity values fall above the theoretical curves. This means that
some of the thin cracks that exist in-situ were not imaged, likely
because the resolution was not fine enough for this purpose. This
also means that the permeability computed on the same digital
sample may possibly be smaller than in-situ. The next step is to
process this original image to introduce the cracks that affect the
elastic properties of this sample. The five versions of the new
image are shown in FIG. 14 where the crack porosity was gradually
increased from zero in the original sample to about 0.06 in the
image with pervasive cracks (FIG. 14). As a result, the total
porosity of this digital sample increased from 0.105 to 0.165. The
P- and S-wave velocity computed on these five samples are plotted
versus the total porosity in FIGS. 17A-B. The permeability-velocity
plots for these digital data are shown in FIGS. 18A-B. The
permeability was computed in three directions (x, y, z). These
permeability values are represented by the squares (x)(i.e., the
squares having the lowest permeability values for the first four Vp
and Vs values (moving left to right in the plots), and the squares
having the intermediate permeability values for the highest two Vp
and Vs values at the right side of the plots); squares (y) (i.e.,
the squares having the intermediate permeability values for the
first four Vp and Vs values (moving left to right in the plots),
and the square having the lowest permeability value for the highest
Vp and Vs values at the right side of the plots); and squares (z)
(i.e., the squares having the highest permeability values for all
the Vp and Vs values (moving left to right in the plots).
It can be concluded that the directional permeability computed on
these two digital samples (FIGS. 19A-C) may fall within the
expected in situ range (FIGS. 20A-C). This permeability is about
0.00040 mD in the x direction, 0.00063 mD in the y direction, and
0.00125 mD in the z direction.
On some occasions, the velocity computed on a digital sample may
fall below the criterion velocity-porosity curve, as in the tight
gas sandstone (TGS) sample with the computed porosity and velocity
shown in FIGS. 21A-B. This means that some of the cracks generated
due to the unloading of the sample do appear in the image of the
sample.
One way of dealing with this situation is to process the original
image to close these cracks. Another way is to subsample this
sample by, e.g., evenly subdividing it into eight
(2.times.2.times.2) subsamples (FIG. 22) and compute the porosity
and velocity on each of these subsamples with the prospect that
some of the results will fall onto the criterion velocity-porosity
curve. If so, then the permeability computed on these subsamples
can be considered to fall into the in-situ range. If not,
techniques can be use to heal the cracks. Image cracks can be
closed or healed using the method such as described in U.S. Patent
Application Publication No. 2010/0131204 A1, especially the
discussion related to FIGS. 12A-12D therein, which is incorporated
herein by reference in its entirety. This procedure allows removal
of thin cracks, which may have been the result of damage to the
rock sample from the drill bit, inelastic stress release and
drying.
The porosity and velocity computed on the eight subsamples of the
original TGS sample are displayed in FIGS. 21A-B. Three of these
eight data points meet the velocity porosity criterion, and these
are the subsamples having data points that fall on the curve shown
as a black line. This means that the permeability computed on these
three subsamples falls into the in-situ permeability range (FIGS.
23A-C).
The porosities of these three subsamples differ from that of the
original sample. To adjust the permeability of the original sample
for the in-situ stress conditions, line 11 is fit to the three
subsample data points in FIG. 23C and the permeability on this line
is computed at the porosity of the original sample. The result is
that in the original sample whose computed porosity and
permeability (in the x direction) are 0.079 and 0.456 mD,
respectively, the permeability adjusted to the in-situ conditions
becomes 0.170 mD with a reduction factor about 3.
As demonstrated by these findings, the inability to directly image
a rock sample at in-situ stress conditions does not hinder the
ability of predicting rock properties at in-situ conditions. This
can be accomplished by processing the image and re-computing its
physical properties with objective rock physics criteria in mind.
The main assumption behind the approach herein is that if one
physical attribute of a digital image (the elastic properties)
falls within a theoretically established in-situ value range, all
other attributes (porosity, permeability, and electrical
resistivity) computed on the same image are also expected to fall
within the in-situ range.
Step 115
With respect to computational transforms used for the computation
engines, the computation transform used can be the Lattice Boltzman
method for multiphase fluid simulation, which is a computational
transform that can be used to compute transport properties such as
absolute and relative permeability. Properties such as viscosity,
capillary tension of the fluids, wettability are determined or
inferred by the logging tool such as MDT. As indicated, an
estimation of relative permeability based only on in-situ fluid
composition techniques is indirect. That is, the detailed structure
of the formation pore structure would not be known from such
downhole logging tool measurements. In the present methods, the
rock structure (pores and grains) are input from digital rock
physics. Direct calculation of relative permeability is made
possible, for example, by applying the Lattice Boltzman numerical
method to the 3D pore structure of the formation that is identified
within the segmented image of rock and using the pore-fluid
properties measured at the interval of investigation by the logging
tool.
FIG. 24 shows an exemplary workflow for computation of rock
properties using properties estimated from well log data and
computed from digital rock physics in an integrated manner in
accordance with the present invention.
Step 116
The estimated rock properties derived from in situ logging
measurements and computed individual rock properties from digital
rock physics can be compared in several ways. They can simply be
compared, value to value. Several pairs of values from different
measurements of the same or different rock locations can be cross
plotted. For example, the logging tool estimates may be plotted on
the digital trend graph as shown in FIG. 4.
Step 117
The analysis part of step 117 is different from the comparing step
116 in that conclusions or implications are drawn from the
comparisons. The quadrant method shown in FIG. 4, for example, is a
defined analysis method for this step. It is expected that this
area can be further refined as the empirical database is expanded,
such as when greater numbers of actual samples are run and
comparisons/analyses are performed.
Referring again to FIG. 10, an example of the indicated trend
method is shown in more detail. Many of the series 100 steps shown
in FIG. 9 are equally applicable to the corresponding series 200
steps of the method shown in FIG. 10 other than steps 213, 217, and
218 in particular. For example, steps 201, 202, 203, 204, 205, 206,
207, 208, 209, 210, 211, 212, 214, 215, and 216 in FIG. 10 can be
similar to steps 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,
111, 112, 113, 114, and 115 in FIG. 9, respectively.
Step 213
To subsample the 3D segmented matrix, a method can be used such as
disclosed with respect to FIG. 22, or the methods shown in U.S.
Patent Application Publication Nos. 2010/0128932 A1 (e.g.,
paragraph [0041], FIGS. 3) and 2010/0131204 A1 (e.g., paragraph
[0058], FIGS. 7-8), which are incorporated herein by reference in
their entireties. For example, to obtain a sufficient number of
data points to establish relationships between porosity and other
petrophysical parameters, the original segmented image volume may
be subdivided into a selected number of sub-volumes, as shown at
FIG. 22. Subdividing the image can be performed by dividing the
original volume into a number of evenly spaced volumes or by
randomly selecting a sufficient number of sub-volumes. One example
is to divide a cubic image volume into sub-cubes. Examples of
sub-cubes include dividing the original image volume into eight,
twenty seven, sixty four or one hundred twenty five cubic
sub-volumes. A value of porosity may be determined for each
sub-volume.
Step 217
The rock physics relationships can be established by plotting one
property versus another, for example, absolute permeability versus
porosity.
Step 218
The upscaling can be used to predict rock property relationships
outside the range of variation found in the rock sample itself.
This is used to produce the trend graph such as shown in FIG.
4.
A program product can be stored on a computer-readable medium,
which when executed, enables a computer infrastructure to perform
at least steps 114-117 of the point method (e.g., FIG. 9), or at
least steps 215-219 of the trend method (e.g., FIG. 10), to
integrate individual properties of downhole fluid properties
measured or determined and digital rock physics of an earth
formation of a reservoir. To this extent, the computer-readable
medium includes program code, which implements the process
described herein. It is understood that the term "computer-readable
medium" comprises one or more of any type of physical embodiment of
the program code. In particular, the computer-readable medium can
comprise program code embodied on one or more portable storage
articles of manufacture (e.g., a compact disc, a memory stick or
other portable device, a magnetic disk, a tape, flash memory,
etc.), on one or more data storage portions of a computing device,
such as memory and/or other storage system, and/or as a data signal
traveling over a network (e.g., during a wired/wireless electronic
distribution of the program product). To this extent, the
deployment of the program product can comprise one or more of: (1)
installing program code on a computing device, such as computer 27
(FIG. 1), from a computer-readable medium; (2) adding one or more
computing devices to the computer infrastructure; and (3)
incorporating and/or modifying one or more existing systems of the
computer infrastructure to enable the computer infrastructure to
perform the processes of the invention. As used herein, it is
understood that the terms "program code" and "computer program
code" are synonymous and mean any expression, in any language,
code, or notation, of a set of instructions that cause a computing
device having an information processing capability to perform a
particular function either directly or after any combination of the
following: (a) conversion to another language, code or notation;
(b) reproduction in a different material form; and/or (c)
decompression. To this extent, program code can be embodied as one
or more types of program products, such as an application/software
program, component software/a library of functions, an operating
system, a basic I/O system/driver for a particular computing and/or
I/O device, and the like. Further, it is understood that the terms
"component" and "system" are synonymous as used herein and
represent any combination of hardware and/or software capable of
performing some function(s).
The present invention includes the following
aspects/embodiments/features in any order and/or in any
combination:
1. The present invention relates to a method for making estimates
of subterranean rock properties, comprising a) positioning a
logging tool inside a well bore, b) measuring in situ fluid
properties in at least one well interval in a well using the
logging tool, c) measuring in situ well properties in the well
interval in the well using the logging tool, d) estimating rock
properties in the well for a location of the logging tool in the
well interval using the measured in situ well properties, e)
retrieving at least one rock sample from the well interval in the
well, f) preparing said at least one rock sample for digital rock
physics analysis, g) scanning the at least one rock sample to
produce a digital image of said rock sample, h) segmenting said
digital image of said rock sample to define pores and grains in
said digital image, i) adjusting said digital image to represent
said rock properties at in-situ conditions using said well
properties, j) calculating rock properties from said adjusted
digital image of said rock sample using said in situ fluid
properties, and k) comparing said rock properties at the well
interval where the logging tool was positioned and said rock
properties from said digital image of said rock sample using said
in situ fluid properties.
2. The method of any preceding or following
embodiment/feature/aspect, further comprising l) at least one of
storing, displaying, and printing results of said comparing.
3. The method of any preceding or following
embodiment/feature/aspect, further comprising m) extracting at
least one of fluid and gaseous contents of a subsurface reservoir
at or adjacent said well interval based on results of said
comparing.
4. The method of any preceding or following
embodiment/feature/aspect, wherein said rock sample is produced by
rotary core, percussion, or combinations thereof.
5. The method of any preceding or following
embodiment/feature/aspect, wherein said fluid properties are
temperature, pressure, viscosity, chemical composition, or any
combination thereof.
6. The method of any preceding or following
embodiment/feature/aspect, wherein said in situ well properties are
downhole images, well bore gauge, temperature, pressure,
resistivity, gamma, neutron-density, T1 and T2 relaxation times
from NMR, or any combination thereof.
7. The method of any preceding or following
embodiment/feature/aspect, wherein said rock properties comprise
absolute permeability, total porosity, connected porosity, relative
permeability, capillary pressure, m and n Archies constants,
elastic moduli, or electrical properties, or any combinations
thereof.
8. The method of any preceding or following
embodiment/feature/aspect, further comprising conducting an in situ
pressure transient test during the well interval.
9. The method of any preceding or following
embodiment/feature/aspect, further comprising a digital simulation
of said in situ pressure transient test.
10. The method of any preceding or following
embodiment/feature/aspect, further comprising a comparison of said
in situ pressure transient test and said digital simulation of said
in situ pressure transient test.
11. The method of any preceding or following
embodiment/feature/aspect, wherein said rock sample has a diameter
size of about 2 cm or less and a length of about 2 cm or less.
12. The method of any preceding or following
embodiment/feature/aspect, wherein step k) comprises comparing (i)
a logging tool data point plotted as absolute permeability versus
porosity for the rock properties estimated from the in situ well
properties measured with the logging tool in the well interval,
with (ii) digital rock physics data points plotted as absolute
permeability versus porosity for the rock properties calculated
from said adjusted digital images, and selecting the digital rock
physics data point which is closest to the logging tool data point,
and further comprising l) using the selected digital rock physics
point in at least one subsequent digital rock physics
calculation.
13. A method for estimating subterranean rock properties of a rock
formation comprising a) positioning a logging tool at more than one
location inside a well bore, b) measuring in situ fluid properties
in a well using a logging tool at said more than one location, c)
measuring in situ well properties at said more than one location,
d) estimating rock properties in the well at said more than one
location, e) retrieving rock samples from approximately each of
said more than one locations, f) preparing said rock samples for
digital rock physics analysis, g) scanning said rock samples to
produce digital images of said rock samples, h) subdividing said
digital images into 8 or more digital sub-images, i) segmenting
said digital sub-images to define pores and grains in said digital
sub-images, j) adjusting said digital sub-images to represent said
rock properties at in situ conditions using said well properties,
k) establishing rock physics relations comprising trends,
transforms, models, or any combinations thereof, l) expanding said
rock physics relations beyond the range of parameters measured in
said trends, transforms, models or any combinations thereof, m)
comparing said rock properties in the well approximately at the
more than one location of the logging tool and said rock properties
from said digital image of said rock sample using said in situ
fluid properties, n) selecting one or more rock properties measured
in the well or core sample, and o) reconstructing additional rock
properties by applying said rock physics relations trends,
transforms and models to said one or more rock properties measured
in the well or core sample.
14. The method of any preceding or following
embodiment/feature/aspect, wherein said rock sample is produced by
rotary core or percussion.
15. The method of any preceding or following
embodiment/feature/aspect, wherein said fluid properties comprise
temperature, pressure, viscosity, chemical composition, or any
combinations thereof.
16. The method of any preceding or following
embodiment/feature/aspect, wherein said in situ well properties
comprise downhole images, well bore gauge, temperature, pressure,
resistivity, gamma, neutron-density, T1 and T2 relaxation times
from NMR, or any combinations thereof.
17. The method of any preceding or following
embodiment/feature/aspect, wherein said rock properties comprise
absolute permeability, total porosity, connected porosity, relative
permeability, capillary pressure, m and n Archies constants,
elastic moduli, electrical properties, or any combinations
thereof.
18. The method of any preceding or following
embodiment/feature/aspect, wherein said rock physics relations
comprise at least one relationship between velocity, porosity, and
mineralogy; permeability, porosity, and mineralogy; electrical
formation factor, porosity and mineralogy; and relative
permeability and saturation.
19. The method of any preceding or following
embodiment/feature/aspect, wherein said rock sample has a diameter
size of about 2 cm or less or a length of about 2 cm or less.
20. A system for making estimates of subterranean rock properties,
comprising a) a logging tool positionable inside a well borehole
and operable for measuring in situ fluid and well properties in at
least one well interval in a well, b) at least one computer
processor programmable for estimating rock properties in the well
for a location of the logging tool in the well interval using the
measured in situ well properties, c) a sampling tool operable for
retrieving at least one rock sample from a rock formation bounding
the well borehole in the well interval, and d) a CT or SEM scanner
operable to produce digital images of at least one retrieved rock
sample from the formation, and e) at least one computer processor
programmable for segmenting said digital images of said rock sample
to define pores and grains in said digital image, adjusting said
digital image to represent said rock properties at in-situ
conditions using said well properties, calculating rock properties
from said adjusted digital image of said rock sample using said in
situ fluid properties, and comparing said rock properties at the
well interval where the logging tool being positionable and said
rock properties from said digital image of said rock sample using
said in situ fluid properties.
21. The system of any preceding or following
embodiment/feature/aspect, wherein the logging tool comprises an
MDT tool and the sampling tool comprises a sidewall rotary coring
tool.
22. A computer program product on a computer readable medium that,
when performed on a processor in a computerized device provides a
method for performing computations of at least one of steps (h),
(i), (j), and (k) of claim 1.
The present invention can include any combination of these various
features or embodiments above and/or below as set forth in
sentences and/or paragraphs. Any combination of disclosed features
herein is considered part of the present invention and no
limitation is intended with respect to combinable features.
EXAMPLES
Example 1
An example of rebuilding the physical properties of rock of a well
interval, such as from core or drill cuttings retrieved from the
interval of interest, is the following.
1. Collect a number of relatively large (.about.1 mm) cuttings or
core samples from the interval of interest where logging has been
conducted or a core extracted.
2. Produce a CT Image of the cuttings and segment these images.
3. Bring these images to in-situ conditions.
4. By sub-sampling the resulting digital volumes, establish rock
physics relations (trends, transforms, or models) between velocity,
porosity, and mineralogy; permeability, porosity, and mineralogy;
electrical formation factor versus porosity and mineralogy;
relative permeability and saturation. Theoretically substantiate
and expand these models.
5. Select one or more of parameters measured in the well (e.g., the
bulk density and/or elastic-wave velocity) or on the core (the bulk
density and electronic number) and then, by applying the rock
physics relations established on the cuttings or core samples,
reconstruct (rebuild) the full suite of the physical properties of
the logged interval or extracted core.
Following is an example of populating the subsurface with rock
properties not directly measured in-situ for seismic
interpretation.
1. Rock physics relations (trends, transforms, or models) are
established by combining a set of controlled experiments and rock
physics theory. These controlled experiments are conducted in the
computational rock physics laboratory on a set of drill
cuttings.
2. The rock physics relations (trends) are established and
upscaled. Then these upscaled relations are applied to the elastic
properties inferred from seismic data to populate the seismic
volumes of interest with the missing properties.
Applicants specifically incorporate the entire contents of all
cited references in this disclosure. Further, when an amount,
concentration, or other value or parameter is given as either a
range, preferred range, or a list of upper preferable values and
lower preferable values, this is to be understood as specifically
disclosing all ranges formed from any pair of any upper range limit
or preferred value and any lower range limit or preferred value,
regardless of whether ranges are separately disclosed. Where a
range of numerical values is recited herein, unless otherwise
stated, the range is intended to include the endpoints thereof, and
all integers and fractions within the range. It is not intended
that the scope of the invention be limited to the specific values
recited when defining a range.
Other embodiments of the present invention will be apparent to
those skilled in the art from consideration of the present
specification and practice of the present invention disclosed
herein. It is intended that the present specification and examples
be considered as exemplary only with a true scope and spirit of the
invention being indicated by the following claims and equivalents
thereof.
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